Hyperplane distance neighbor clustering based on local discriminant analysis for complex chemical processes monitoring

International Nuclear Information System (INIS)

Lu, Chunhong; Xiao, Shaoqing; Gu, Xiaofeng

2014-01-01

The collected training data often include both normal and faulty samples for complex chemical processes. However, some monitoring methods, such as partial least squares (PLS), principal component analysis (PCA), independent component analysis (ICA) and Fisher discriminant analysis (FDA), require fault-free data to build the normal operation model. These techniques are applicable after the preliminary step of data clustering is applied. We here propose a novel hyperplane distance neighbor clustering (HDNC) based on the local discriminant analysis (LDA) for chemical process monitoring. First, faulty samples are separated from normal ones using the HDNC method. Then, the optimal subspace for fault detection and classification can be obtained using the LDA approach. The proposed method takes the multimodality within the faulty data into account, and thus improves the capability of process monitoring significantly. The HDNC-LDA monitoring approach is applied to two simulation processes and then compared with the conventional FDA based on the K-nearest neighbor (KNN-FDA) method. The results obtained in two different scenarios demonstrate the superiority of the HDNC-LDA approach in terms of fault detection and classification accuracy

Identification of new candidate drugs for lung cancer using chemical-chemical interactions, chemical-protein interactions and a K-means clustering algorithm.

Science.gov (United States)

Lu, Jing; Chen, Lei; Yin, Jun; Huang, Tao; Bi, Yi; Kong, Xiangyin; Zheng, Mingyue; Cai, Yu-Dong

2016-01-01

Lung cancer, characterized by uncontrolled cell growth in the lung tissue, is the leading cause of global cancer deaths. Until now, effective treatment of this disease is limited. Many synthetic compounds have emerged with the advancement of combinatorial chemistry. Identification of effective lung cancer candidate drug compounds among them is a great challenge. Thus, it is necessary to build effective computational methods that can assist us in selecting for potential lung cancer drug compounds. In this study, a computational method was proposed to tackle this problem. The chemical-chemical interactions and chemical-protein interactions were utilized to select candidate drug compounds that have close associations with approved lung cancer drugs and lung cancer-related genes. A permutation test and K-means clustering algorithm were employed to exclude candidate drugs with low possibilities to treat lung cancer. The final analysis suggests that the remaining drug compounds have potential anti-lung cancer activities and most of them have structural dissimilarity with approved drugs for lung cancer.

Clustering methods for the optimization of atomic cluster structure

Science.gov (United States)

Bagattini, Francesco; Schoen, Fabio; Tigli, Luca

2018-04-01

In this paper, we propose a revised global optimization method and apply it to large scale cluster conformation problems. In the 1990s, the so-called clustering methods were considered among the most efficient general purpose global optimization techniques; however, their usage has quickly declined in recent years, mainly due to the inherent difficulties of clustering approaches in large dimensional spaces. Inspired from the machine learning literature, we redesigned clustering methods in order to deal with molecular structures in a reduced feature space. Our aim is to show that by suitably choosing a good set of geometrical features coupled with a very efficient descent method, an effective optimization tool is obtained which is capable of finding, with a very high success rate, all known putative optima for medium size clusters without any prior information, both for Lennard-Jones and Morse potentials. The main result is that, beyond being a reliable approach, the proposed method, based on the idea of starting a computationally expensive deep local search only when it seems worth doing so, is capable of saving a huge amount of searches with respect to an analogous algorithm which does not employ a clustering phase. In this paper, we are not claiming the superiority of the proposed method compared to specific, refined, state-of-the-art procedures, but rather indicating a quite straightforward way to save local searches by means of a clustering scheme working in a reduced variable space, which might prove useful when included in many modern methods.

Integration K-Means Clustering Method and Elbow Method For Identification of The Best Customer Profile Cluster

Science.gov (United States)

Syakur, M. A.; Khotimah, B. K.; Rochman, E. M. S.; Satoto, B. D.

2018-04-01

Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects. SMEs Indonesia has a variety of customers, but SMEs do not have the mapping of these customers so they did not know which customers are loyal or otherwise. Customer mapping is a grouping of customer profiling to facilitate analysis and policy of SMEs in the production of goods, especially batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is sensitive to the selection of the starting position from the midpoint of the cluster. So choosing the starting position from the midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number of clusters. So Elbow looks for the best number of clusters on the K-means method. Based on the results obtained from the process in determining the best number of clusters with elbow method can produce the same number of clusters K on the amount of different data. The result of determining the best number of clusters with elbow method will be the default for characteristic process based on case study. Measurement of k-means value of k-means has resulted in the best clusters based on SSE values on 500 clusters of batik visitors. The result shows the cluster has a sharp decrease is at K = 3, so K as the cut-off point as the best cluster.

Domino effects within a chemical cluster: a game-theoretical modeling approach by using Nash-equilibrium.

Science.gov (United States)

Reniers, Genserik; Dullaert, Wout; Karel, Soudan

2009-08-15

Every company situated within a chemical cluster faces domino effect risks, whose magnitude depends on every company's own risk management strategies and on those of all others. Preventing domino effects is therefore very important to avoid catastrophes in the chemical process industry. Given that chemical companies are interlinked by domino effect accident links, there is some likelihood that even if certain companies fully invest in domino effects prevention measures, they can nonetheless experience an external domino effect caused by an accident which occurred in another chemical enterprise of the cluster. In this article a game-theoretic approach to interpret and model behaviour of chemical plants within chemical clusters while negotiating and deciding on domino effects prevention investments is employed.

Cluster analysis to evaluate stable chemical elements and physical-chemical parameters behavior on uranium mining waste

International Nuclear Information System (INIS)

Pereira, Wagner de Souza; Py Junior, Delcy de Azevedo; Goncalves, Simone; Kelecom, Alphonse; Morais, Gustavo Ferrari de; Campelo, Emanuele Lazzaretti Cordova; Dores, Luis Augusto de Carvalho Bresser

2011-01-01

The Ore Treating Unit (UTM, in portuguese) is a deactivated uranium mine. A cluster analysis was used to evaluate the behavior of stable chemical elements and physical-chemical parameters in their effluents. The utilization of the cluster analysis proved itself effective in the assessment, allowing the identification of groups of chemical elements, physical-chemical parameters and their joint analysis (elements and parameters). As a result we may assert, based on data analysis, that there is a strong link between calcium and magnesium and between aluminum and rare-earth oxides on UTM's effluents. Sulphate was also identified as strongly linked to total and dissolved solids, and those to electrical conductivity. There were other associations, but not so strongly linked. Further gathering, to seasonal evaluation, are required in order to confirm those analysis. Additional statistical analysis (factor analysis) must be used to try to identify the origin of the identified groups on this analysis. (author)

Cluster analysis to evaluate stable chemical elements and physical-chemical parameters behavior on uranium mining waste

Energy Technology Data Exchange (ETDEWEB)

Pereira, Wagner de Souza; Py Junior, Delcy de Azevedo; Goncalves, Simone, E-mail: wspereira@inb.gov.br [Unidade de Tratamento de Minerio (UTM/INB), Pocos de Caldas, MG (Brazil). Coordenacao de Protecao Radiologica. Grupo Multidisciplinar de Radioprotecao; Kelecom, Alphonse [Universidade Federal Fluminense (UFF), Niteroi, RJ (Brazil). Inst. de Biologia. Lab. de Radiobiologia e Radiometria Pedro Lopes dos Santos; Morais, Gustavo Ferrari de; Campelo, Emanuele Lazzaretti Cordova [Unidade de Tratamento de Minerio (UTM/INB), Pocos de Caldas, MG (Brazil). Coordenacao de Desenvolvimento de Processos; Dores, Luis Augusto de Carvalho Bresser [Unidade de Tratamento de Minerio (UTM/INB), Pocos de Caldas, MG (Brazil). Gerencia de Descomissionamento

2011-07-01

The Ore Treating Unit (UTM, in portuguese) is a deactivated uranium mine. A cluster analysis was used to evaluate the behavior of stable chemical elements and physical-chemical parameters in their effluents. The utilization of the cluster analysis proved itself effective in the assessment, allowing the identification of groups of chemical elements, physical-chemical parameters and their joint analysis (elements and parameters). As a result we may assert, based on data analysis, that there is a strong link between calcium and magnesium and between aluminum and rare-earth oxides on UTM's effluents. Sulphate was also identified as strongly linked to total and dissolved solids, and those to electrical conductivity. There were other associations, but not so strongly linked. Further gathering, to seasonal evaluation, are required in order to confirm those analysis. Additional statistical analysis (factor analysis) must be used to try to identify the origin of the identified groups on this analysis. (author)

Chemical study of the metal-rich globular cluster NGC 5927

Science.gov (United States)

Mura-Guzmán, A.; Villanova, S.; Muñoz, C.; Tang, B.

2018-03-01

Globular clusters (GCs) are natural laboratories where stellar and chemical evolution can be studied in detail. In addition, their chemical patterns and kinematics can tell us to which Galactic structure (disc, bulge, halo or extragalactic) the cluster belongs to. NGC 5927 is one of most metal-rich GCs in the Galaxy and its kinematics links it to the thick disc. We present abundance analysis based on high-resolution spectra of seven giant stars. The data were obtained using Fibre Large Array Multi Element Spectrograph/Ultraviolet Echelle Spectrograph (UVES) spectrograph mounted on UT2 telescope of the European Southern Observatory. The principal objective of this work is to perform a wide and detailed chemical abundance analysis of the cluster and look for possible Multiple Populations (MPs). We determined stellar parameters and measured 22 elements corresponding to light (Na, Al), alpha (O, Mg, Si, Ca, Ti), iron-peak (Sc, V, Cr, Mn, Fe, Co, Ni, Cu, Zn), and heavy elements (Y, Zr, Ba, Ce, Nd, Eu). We found a mean iron content of [Fe/H] = -0.47 ± 0.02 (error on the mean). We confirm the existence of MPs in this GC with an O-Na anti-correlation, and moderate spread in Al abundances. We estimate a mean [α/Fe] = 0.25 ± 0.08. Iron-peak elements show no significant spread. The [Ba/Eu] ratios indicate a predominant contribution from SNeII for the formation of the cluster.

Tracing the Chemical Evolution of Metal-rich Galactic Bulge Globular Clusters

Science.gov (United States)

Munoz Gonzalez, Cesar; Saviane, Ivo; Geisler, Doug; Villanova, Sandro

2018-01-01

We present in this poster the metallicity characterization of the four metal rich Bulge Galactic Gobular Clusters, which have controversial metallicities. We analyzed our high-resolution spectra (using UVES-580nm and GIRAFFE-HR13 setups) for a large sample of RGB/AGB targets in each cluster in order to measure their metallicity and prove or discard the iron spread hypothesis. We have also characterized chemically stars with potentially different iron content by measuring light (O, Na, Mg, Al), alpha (Si, Ca, Ti), iron–peak (V, Cr, Ni, Mn) and s and r process (Y, Zr, Ba, Eu) elements. We have identified possible channels responsible for the chemical heterogeneity of the cluster populations, like AGB or massive fast-rotating stars contamination, or SN explosion. Also, we have analyzed the origin and evolution of these bulge GCs and their connection with the bulge itself.

Chemically induced magnetism in atomically precise gold clusters.

Science.gov (United States)

Krishna, Katla Sai; Tarakeshwar, Pilarisetty; Mujica, Vladimiro; Kumar, Challa S S R

2014-03-12

Comparative theoretical and experimental investigations are reported into chemically induced magnetism in atomically-precise, ligand-stabilized gold clusters Au25 , Au38 and Au55 . The results indicate that [Au25 (PPh3 )10 (SC12 H25 )5 Cl2 ](2+) and Au38 (SC12 H25 )24 are diamagnetic, Au25 (SC2 H4 Ph)18 is paramagnetic, and Au55 (PPh3 )12 Cl6 , is ferromagnetic at room temperature. Understanding the magnetic properties resulting from quantum size effects in such atomically precise gold clusters could lead to new fundamental discoveries and applications. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Effect of ruthenium cluster on the photoluminescence of chemically derivatized porous silicon

International Nuclear Information System (INIS)

Boukherroub, Rabah; Zargarian, Davit; Reber, Christian; Lockwood, David J.; Carty, Arthur J.; Wayner, Danial D.M.

2003-01-01

A green photoluminescent triruthenium cluster (I) has been chemisorbed on highly luminescent porous silicon (PSi) layers, either freshly prepared or chemically modified with 1-decene, ethyl undecylenate, or undecylenic acid, in order to study the influence of the cluster on the photoluminescence (PL) arising from the PSi. Immersing the hydrogen-terminated PSi in a hexane solution of (I) for several days at room temperature led to the quenching of PL arising from PSi; the only PL detected was due to the Ru cluster (I). A complete quenching of the PL due to PSi was also observed when derivatized PSi surfaces were exposed to the same solution of (I); in these cases, the PL of (I) also shifted to lower energies. Both the quenching of the PL arising from the PSi layers and the shift in the PL of the cluster (I) are likely due to the difference in the chemical interaction of the PO moiety and the CO groups of the Ru cluster with the terminal functional group of the organic monolayer

Effect of ruthenium cluster on the photoluminescence of chemically derivatized porous silicon

Energy Technology Data Exchange (ETDEWEB)

Boukherroub, Rabah; Zargarian, Davit; Reber, Christian; Lockwood, David J.; Carty, Arthur J.; Wayner, Danial D.M

2003-07-15

A green photoluminescent triruthenium cluster (I) has been chemisorbed on highly luminescent porous silicon (PSi) layers, either freshly prepared or chemically modified with 1-decene, ethyl undecylenate, or undecylenic acid, in order to study the influence of the cluster on the photoluminescence (PL) arising from the PSi. Immersing the hydrogen-terminated PSi in a hexane solution of (I) for several days at room temperature led to the quenching of PL arising from PSi; the only PL detected was due to the Ru cluster (I). A complete quenching of the PL due to PSi was also observed when derivatized PSi surfaces were exposed to the same solution of (I); in these cases, the PL of (I) also shifted to lower energies. Both the quenching of the PL arising from the PSi layers and the shift in the PL of the cluster (I) are likely due to the difference in the chemical interaction of the PO moiety and the CO groups of the Ru cluster with the terminal functional group of the organic monolayer.

Effect of ruthenium cluster on the photoluminescence of chemically derivatized porous silicon

Science.gov (United States)

Boukherroub, Rabah; Zargarian, Davit; Reber, Christian; Lockwood, David J.; Carty, Arthur J.; Wayner, Danial D. M.

2003-07-01

A green photoluminescent triruthenium cluster (I) has been chemisorbed on highly luminescent porous silicon (PSi) layers, either freshly prepared or chemically modified with 1-decene, ethyl undecylenate, or undecylenic acid, in order to study the influence of the cluster on the photoluminescence (PL) arising from the PSi. Immersing the hydrogen-terminated PSi in a hexane solution of (I) for several days at room temperature led to the quenching of PL arising from PSi; the only PL detected was due to the Ru cluster (I). A complete quenching of the PL due to PSi was also observed when derivatized PSi surfaces were exposed to the same solution of (I); in these cases, the PL of (I) also shifted to lower energies. Both the quenching of the PL arising from the PSi layers and the shift in the PL of the cluster (I) are likely due to the difference in the chemical interaction of the PO moiety and the CO groups of the Ru cluster with the terminal functional group of the organic monolayer.

Genetic k-means clustering approach for mapping human vulnerability to chemical hazards in the industrialized city: a case study of Shanghai, China.

Science.gov (United States)

Shi, Weifang; Zeng, Weihua

2013-06-20

Reducing human vulnerability to chemical hazards in the industrialized city is a matter of great urgency. Vulnerability mapping is an alternative approach for providing vulnerability-reducing interventions in a region. This study presents a method for mapping human vulnerability to chemical hazards by using clustering analysis for effective vulnerability reduction. Taking the city of Shanghai as the study area, we measure human exposure to chemical hazards by using the proximity model with additionally considering the toxicity of hazardous substances, and capture the sensitivity and coping capacity with corresponding indicators. We perform an improved k-means clustering approach on the basis of genetic algorithm by using a 500 m × 500 m geographical grid as basic spatial unit. The sum of squared errors and silhouette coefficient are combined to measure the quality of clustering and to determine the optimal clustering number. Clustering result reveals a set of six typical human vulnerability patterns that show distinct vulnerability dimension combinations. The vulnerability mapping of the study area reflects cluster-specific vulnerability characteristics and their spatial distribution. Finally, we suggest specific points that can provide new insights in rationally allocating the limited funds for the vulnerability reduction of each cluster.

Genetic k-Means Clustering Approach for Mapping Human Vulnerability to Chemical Hazards in the Industrialized City: A Case Study of Shanghai, China

Directory of Open Access Journals (Sweden)

Weihua Zeng

2013-06-01

Full Text Available Reducing human vulnerability to chemical hazards in the industrialized city is a matter of great urgency. Vulnerability mapping is an alternative approach for providing vulnerability-reducing interventions in a region. This study presents a method for mapping human vulnerability to chemical hazards by using clustering analysis for effective vulnerability reduction. Taking the city of Shanghai as the study area, we measure human exposure to chemical hazards by using the proximity model with additionally considering the toxicity of hazardous substances, and capture the sensitivity and coping capacity with corresponding indicators. We perform an improved k-means clustering approach on the basis of genetic algorithm by using a 500 m × 500 m geographical grid as basic spatial unit. The sum of squared errors and silhouette coefficient are combined to measure the quality of clustering and to determine the optimal clustering number. Clustering result reveals a set of six typical human vulnerability patterns that show distinct vulnerability dimension combinations. The vulnerability mapping of the study area reflects cluster-specific vulnerability characteristics and their spatial distribution. Finally, we suggest specific points that can provide new insights in rationally allocating the limited funds for the vulnerability reduction of each cluster.

Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

International Nuclear Information System (INIS)

Anderson, Ryan B.; Bell, James F.; Wiens, Roger C.; Morris, Richard V.; Clegg, Samuel M.

2012-01-01

We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO 2 at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by ∼ 3 wt.%. The statistical significance of these improvements was ∼ 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and specifically

A sequential-move game for enhancing safety and security cooperation within chemical clusters

International Nuclear Information System (INIS)

Pavlova, Yulia; Reniers, Genserik

2011-01-01

The present paper provides a game theoretic analysis of strategic cooperation on safety and security among chemical companies within a chemical industrial cluster. We suggest a two-stage sequential move game between adjacent chemical plants and the so-called Multi-Plant Council (MPC). The MPC is considered in the game as a leader player who makes the first move, and the individual chemical companies are the followers. The MPC's objective is to achieve full cooperation among players through establishing a subsidy system at minimum expense. The rest of the players rationally react to the subsidies proposed by the MPC and play Nash equilibrium. We show that such a case of conflict between safety and security, and social cooperation, belongs to the 'coordination with assurance' class of games, and we explore the role of cluster governance (fulfilled by the MPC) in achieving a full cooperative outcome in domino effects prevention negotiations. The paper proposes an algorithm that can be used by the MPC to develop the subsidy system. Furthermore, a stepwise plan to improve cross-company safety and security management in a chemical industrial cluster is suggested and an illustrative example is provided.

Probabilistic vulnerability assessment of chemical clusters subjected to external acts of interference

NARCIS (Netherlands)

Argenti, F.; Landucci, G; Reniers, G.L.L.M.E.

2016-01-01

Acts of interference against chemical facilities or chemical clusters might result in severe consequences in
case of a successful attack (major explosions, fires, toxic dispersions or environmental contamination).
Although process facilities implement multiple safety barriers to control

Chemical degradation of selected Zn-based corrosion products induced by C{sub 60} cluster, Ar cluster and Ar{sup +} ion sputtering in the focus of X-ray photoelectron spectroscopy (XPS)

Energy Technology Data Exchange (ETDEWEB)

Steinberger, R., E-mail: roland.steinberger@jku.at [Christian Doppler Laboratory for Microscopic and Spectroscopic Material Characterization, Center for Surface and Nanoanalytics, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz (Austria); Sicking, J., E-mail: jens.sicking@bayer.com [Bayer AG, Engineering & Technology, Applied Physics, Chempark Building E 41, 51368 Leverkusen (Germany); Weise, J., E-mail: juliane.weise@physik.tu-freiberg.de [Institut für Experimentelle Physik, TU Bergakademie Freiberg, Leipziger Straße 23, 09599 Freiberg (Germany); Duchoslav, J., E-mail: jiri.duchoslav@jku.at [Christian Doppler Laboratory for Microscopic and Spectroscopic Material Characterization, Center for Surface and Nanoanalytics, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz (Austria); Greunz, T., E-mail: theresia.greunz@jku.at [Christian Doppler Laboratory for Microscopic and Spectroscopic Material Characterization, Center for Surface and Nanoanalytics, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz (Austria); Meyer, D.C., E-mail: Dirk-Carl.Meyer@physik.tu-freiberg.de [Institut für Experimentelle Physik, TU Bergakademie Freiberg, Leipziger Straße 23, 09599 Freiberg (Germany); Stifter, D., E-mail: david.stifter@jku.at [Christian Doppler Laboratory for Microscopic and Spectroscopic Material Characterization, Center for Surface and Nanoanalytics, Johannes Kepler University Linz, Altenberger Straße 69, 4040 Linz (Austria)

2017-05-01

Highlights: • XPS investigations for various sputter concepts on Zn-based corrosions products. • Direct comparison of induced chemical damage for ion and cluster sputtering. • Azimuthal rotation or heavy projectile bombardment was not found to be beneficial. • Ar cluster etching is rated as unsuitable for surface cleaning or depth profiling. • C{sub 60} and Ar{sup +} are applicable for sputtering when degradation is carefully considered. - Abstract: Monoatomic ion sputtering is a common concept for surface sensitive analysis methods to clean surfaces prior investigation or to obtain information from deeper regions. However, severe damage of the materials – linked to preferential sputtering, ion implantation, atomic mixing and in worst case chemical degradation – can affect the validity of the analysis. Hence, the impact of C{sub 60} cluster etching, furthermore, of Ar{sup +} ion bombardment with and without azimuthal sample rotation and also the application of heavy projectiles (Xe{sup +} ions) was investigated to find a concept, which is less destructive or with less critical influence on the chemical nature of the investigated materials. In this work the focus is set on hydrozincite and zinc oxide, two common corrosion products of Zn-based coatings. As a main point, all the obtained results from (i) Ar{sup +} ion, (ii) Ar cluster, and (iii) C{sub 60} cluster etching on the degradation kinetics of hydrozincite were compared with respect to the reached sputter depth. In addition, the sputter rate of all three methods was experimentally determined for ZnO. In total, fully non-destructive conditions could not be found, but valuable knowledge on the type and rate of degradation, which is essential to choose the most suited sputter concept.

Momentum-space cluster dual-fermion method

Science.gov (United States)

Iskakov, Sergei; Terletska, Hanna; Gull, Emanuel

2018-03-01

Recent years have seen the development of two types of nonlocal extensions to the single-site dynamical mean field theory. On one hand, cluster approximations, such as the dynamical cluster approximation, recover short-range momentum-dependent correlations nonperturbatively. On the other hand, diagrammatic extensions, such as the dual-fermion theory, recover long-ranged corrections perturbatively. The correct treatment of both strong short-ranged and weak long-ranged correlations within the same framework is therefore expected to lead to a quick convergence of results, and offers the potential of obtaining smooth self-energies in nonperturbative regimes of phase space. In this paper, we present an exact cluster dual-fermion method based on an expansion around the dynamical cluster approximation. Unlike previous formulations, our method does not employ a coarse-graining approximation to the interaction, which we show to be the leading source of error at high temperature, and converges to the exact result independently of the size of the underlying cluster. We illustrate the power of the method with results for the second-order cluster dual-fermion approximation to the single-particle self-energies and double occupancies.

Photoluminescence quenching of chemically functionalized porous silicon by a ruthenium cluster

Energy Technology Data Exchange (ETDEWEB)

Boukherroub, R.; Wayner, D.D.M. [Steacie Institute for Molecular Sciences, National Research Council of Canada, Ottawa, Ontario (Canada); Lockwood, D.J. [Institute for Microstructural Sciences, National Research Council of Canada, Ottawa, Ontario (Canada); Zargarian, D. [Chemistry Department, University of Montreal, C.P. 6128, succursale, Centre-ville, Montreal QC (Canada)

2003-05-01

This paper describes photoluminescence (PL) quenching of hydrogen-terminated and chemically derivatized porous silicon (PSi) nanostructures by a green ruthenium cluster (I). Chemisorption of freshly prepared PSi surfaces in a hexane solution of the Ru cluster for several days at room temperature led to a complete quenching of the PSi PL. The only visible PL was due to the original PL of the cluster. When the PSi surface functionalized with undecylenic acid was immersed in the same hexane solution of (I), the PSi PL was completely quenched and accompanied with a shift to a lower energy of the cluster PL. This shift was assigned to the formation of an ester linkage resulting from the nucleophilic attack of the PO anion of the cluster on the terminal acid functional group. (Abstract Copyright [2003], Wiley Periodicals, Inc.)

Photoluminescence quenching of chemically functionalized porous silicon by a ruthenium cluster

Science.gov (United States)

Boukherroub, R.; Wayner, D. D. M.; Lockwood, D. J.; Zargarian, D.

2003-05-01

This paper describes photoluminescence (PL) quenching of hydrogen-terminated and chemically derivatized porous silicon (PSi) nanostructures by a green ruthenium cluster (I). Chemisorption of freshly prepared PSi surfaces in a hexane solution of the Ru cluster for several days at room temperature led to a complete quenching of the PSi PL. The only visible PL was due to the original PL of the cluster. When the PSi surface functionalized with undecylenic acid was immersed in the same hexane solution of (I), the PSi PL was completely quenched and accompanied with a shift to a lower energy of the cluster PL. This shift was assigned to the formation of an ester linkage resulting from the nucleophilic attack of the PO anion of the cluster on the terminal acid functional group.

A sequential-move game for enhancing safety and security cooperation within chemical clusters.

Science.gov (United States)

Pavlova, Yulia; Reniers, Genserik

2011-02-15

The present paper provides a game theoretic analysis of strategic cooperation on safety and security among chemical companies within a chemical industrial cluster. We suggest a two-stage sequential move game between adjacent chemical plants and the so-called Multi-Plant Council (MPC). The MPC is considered in the game as a leader player who makes the first move, and the individual chemical companies are the followers. The MPC's objective is to achieve full cooperation among players through establishing a subsidy system at minimum expense. The rest of the players rationally react to the subsidies proposed by the MPC and play Nash equilibrium. We show that such a case of conflict between safety and security, and social cooperation, belongs to the 'coordination with assurance' class of games, and we explore the role of cluster governance (fulfilled by the MPC) in achieving a full cooperative outcome in domino effects prevention negotiations. The paper proposes an algorithm that can be used by the MPC to develop the subsidy system. Furthermore, a stepwise plan to improve cross-company safety and security management in a chemical industrial cluster is suggested and an illustrative example is provided. Copyright © 2010 Elsevier B.V. All rights reserved.

Clustering and training set selection methods for improving the accuracy of quantitative laser induced breakdown spectroscopy

Energy Technology Data Exchange (ETDEWEB)

Anderson, Ryan B., E-mail: randerson@astro.cornell.edu [Cornell University Department of Astronomy, 406 Space Sciences Building, Ithaca, NY 14853 (United States); Bell, James F., E-mail: Jim.Bell@asu.edu [Arizona State University School of Earth and Space Exploration, Bldg.: INTDS-A, Room: 115B, Box 871404, Tempe, AZ 85287 (United States); Wiens, Roger C., E-mail: rwiens@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States); Morris, Richard V., E-mail: richard.v.morris@nasa.gov [NASA Johnson Space Center, 2101 NASA Parkway, Houston, TX 77058 (United States); Clegg, Samuel M., E-mail: sclegg@lanl.gov [Los Alamos National Laboratory, P.O. Box 1663 MS J565, Los Alamos, NM 87545 (United States)

2012-04-15

We investigated five clustering and training set selection methods to improve the accuracy of quantitative chemical analysis of geologic samples by laser induced breakdown spectroscopy (LIBS) using partial least squares (PLS) regression. The LIBS spectra were previously acquired for 195 rock slabs and 31 pressed powder geostandards under 7 Torr CO{sub 2} at a stand-off distance of 7 m at 17 mJ per pulse to simulate the operational conditions of the ChemCam LIBS instrument on the Mars Science Laboratory Curiosity rover. The clustering and training set selection methods, which do not require prior knowledge of the chemical composition of the test-set samples, are based on grouping similar spectra and selecting appropriate training spectra for the partial least squares (PLS2) model. These methods were: (1) hierarchical clustering of the full set of training spectra and selection of a subset for use in training; (2) k-means clustering of all spectra and generation of PLS2 models based on the training samples within each cluster; (3) iterative use of PLS2 to predict sample composition and k-means clustering of the predicted compositions to subdivide the groups of spectra; (4) soft independent modeling of class analogy (SIMCA) classification of spectra, and generation of PLS2 models based on the training samples within each class; (5) use of Bayesian information criteria (BIC) to determine an optimal number of clusters and generation of PLS2 models based on the training samples within each cluster. The iterative method and the k-means method using 5 clusters showed the best performance, improving the absolute quadrature root mean squared error (RMSE) by {approx} 3 wt.%. The statistical significance of these improvements was {approx} 85%. Our results show that although clustering methods can modestly improve results, a large and diverse training set is the most reliable way to improve the accuracy of quantitative LIBS. In particular, additional sulfate standards and

Improving local clustering based top-L link prediction methods via asymmetric link clustering information

Science.gov (United States)

Wu, Zhihao; Lin, Youfang; Zhao, Yiji; Yan, Hongyan

2018-02-01

Networks can represent a wide range of complex systems, such as social, biological and technological systems. Link prediction is one of the most important problems in network analysis, and has attracted much research interest recently. Many link prediction methods have been proposed to solve this problem with various techniques. We can note that clustering information plays an important role in solving the link prediction problem. In previous literatures, we find node clustering coefficient appears frequently in many link prediction methods. However, node clustering coefficient is limited to describe the role of a common-neighbor in different local networks, because it cannot distinguish different clustering abilities of a node to different node pairs. In this paper, we shift our focus from nodes to links, and propose the concept of asymmetric link clustering (ALC) coefficient. Further, we improve three node clustering based link prediction methods via the concept of ALC. The experimental results demonstrate that ALC-based methods outperform node clustering based methods, especially achieving remarkable improvements on food web, hamster friendship and Internet networks. Besides, comparing with other methods, the performance of ALC-based methods are very stable in both globalized and personalized top-L link prediction tasks.

Risk perception of aquatic pollution originated from chemical industry clusters in the coastal area of Jiangsu province, China.

Science.gov (United States)

Yao, Hong; Liu, Bo; You, Zhen; Zhao, Li

2018-02-01

According to "the Layout Scheme of the Chemical Industry in Jiangsu Province From 2016 to 2030" and "the Development Planning in the Coastal Area of Jiangsu Province, China," several chemical industry clusters will be located in the coastal area of Jiangsu province, China, and the risk of surface water pollution will be inevitably higher in the densely populated region. To get to know the risk acceptance level of the residents near the clusters, public perception was analyzed from the five risk factors: the basic knowledge about the pollution, the negative effects on aquatic environment imposed by the clusters, the positive effects brought by the clusters, the trust of controlling aquatic pollution, and the acceptance of the clusters. Twenty-four statements were screened out to describe the five factors, and about 600 residents were covered in three typical clusters surveyed. On the whole, the youth showed a higher interest on the survey, and middle-aged people were likely to be more concerned about aquatic pollution incident. There was no significant difference on risk perception of the three clusters. The respondents investigated had good knowledge background on aquatic pollution and the residents identified with the benefits brought by the clusters. They were weak in risk awareness of pollution originated from the chemical enterprises' groups. Although the respondents regarded that chemical industry clusters did not expose all points of pollutants' generation to the public, they inclined to trust the administration agencies on controlling the pollution and welcome the construction of chemical clusters in their dwelling cities. Besides, risk perception showed obvious spatial distribution. The closer were the samples' sites to the clusters and the rivers receiving pollutants, the higher were the residents' perceived risk, benefit, and trust. However, there was no identical spatial difference on risk acceptance, which might be comprehensively influenced by various

Progeny Clustering: A Method to Identify Biological Phenotypes

Science.gov (United States)

Hu, Chenyue W.; Kornblau, Steven M.; Slater, John H.; Qutub, Amina A.

2015-01-01

Estimating the optimal number of clusters is a major challenge in applying cluster analysis to any type of dataset, especially to biomedical datasets, which are high-dimensional and complex. Here, we introduce an improved method, Progeny Clustering, which is stability-based and exceptionally efficient in computing, to find the ideal number of clusters. The algorithm employs a novel Progeny Sampling method to reconstruct cluster identity, a co-occurrence probability matrix to assess the clustering stability, and a set of reference datasets to overcome inherent biases in the algorithm and data space. Our method was shown successful and robust when applied to two synthetic datasets (datasets of two-dimensions and ten-dimensions containing eight dimensions of pure noise), two standard biological datasets (the Iris dataset and Rat CNS dataset) and two biological datasets (a cell phenotype dataset and an acute myeloid leukemia (AML) reverse phase protein array (RPPA) dataset). Progeny Clustering outperformed some popular clustering evaluation methods in the ten-dimensional synthetic dataset as well as in the cell phenotype dataset, and it was the only method that successfully discovered clinically meaningful patient groupings in the AML RPPA dataset. PMID:26267476

Hybrid Tracking Algorithm Improvements and Cluster Analysis Methods.

Science.gov (United States)

1982-02-26

UPGMA ), and Ward’s method. Ling’s papers describe a (k,r) clustering method. Each of these methods have individual characteristics which make them...Reference 7), UPGMA is probably the most frequently used clustering strategy. UPGMA tries to group new points into an existing cluster by using an

Zintl Clusters as Wet-Chemical Precursors for Germanium Nanomorphologies with Tunable Composition.

Science.gov (United States)

Bentlohner, Manuel M; Waibel, Markus; Zeller, Patrick; Sarkar, Kuhu; Müller-Buschbaum, Peter; Fattakhova-Rohlfing, Dina; Fässler, Thomas F

2016-02-12

[Ge9](4-) Zintl clusters are used as soluble germanium source for a bottom-up fabrication of Ge nanomorphologies such as inverse opal structures with tunable composition. The method is based on the assembly and oxidation of [Ge9 ](4-) clusters in a template mold using SiCl4 , GeCl4 , and PCl3 leading to Si and P-containing Ge phases as shown by X-ray diffraction, Raman spectroscopy, and energy-dispersive X-ray analysis. [Ge9](4-) clusters are retained using ethylenediamine (en) as a transfer medium to a mold after removal of the solvent if water is thoroughly excluded, but are oxidized to amorphous Ge in presence of water traces. (1)H NMR spectroscopy reveals the oxidative deprotonation of en by [Ge9](4-). Subsequent annealing leads to crystalline Ge. As an example for wet-chemical synthesis of complex Ge nanomorphologies, we describe the fabrication of undoped and P-doped inverse opal-structured Ge films with a rather low oxygen contents. The morphology of the films with regular volume porosity is characterized by SEM, TEM, and grazing incidence small-angle X-ray scattering. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Fuzzy C-means method for clustering microarray data.

Science.gov (United States)

Dembélé, Doulaye; Kastner, Philippe

2003-05-22

Clustering analysis of data from DNA microarray hybridization studies is essential for identifying biologically relevant groups of genes. Partitional clustering methods such as K-means or self-organizing maps assign each gene to a single cluster. However, these methods do not provide information about the influence of a given gene for the overall shape of clusters. Here we apply a fuzzy partitioning method, Fuzzy C-means (FCM), to attribute cluster membership values to genes. A major problem in applying the FCM method for clustering microarray data is the choice of the fuzziness parameter m. We show that the commonly used value m = 2 is not appropriate for some data sets, and that optimal values for m vary widely from one data set to another. We propose an empirical method, based on the distribution of distances between genes in a given data set, to determine an adequate value for m. By setting threshold levels for the membership values, genes which are tigthly associated to a given cluster can be selected. Using a yeast cell cycle data set as an example, we show that this selection increases the overall biological significance of the genes within the cluster. Supplementary text and Matlab functions are available at http://www-igbmc.u-strasbg.fr/fcm/

Understanding Boron through Size-Selected Clusters: Structure, Chemical Bonding, and Fluxionality

Energy Technology Data Exchange (ETDEWEB)

Sergeeva, Alina P.; Popov, Ivan A.; Piazza, Zachary A.; Li, Wei-Li; Romanescu, Constantin; Wang, Lai S.; Boldyrev, Alexander I.

2014-04-15

Conspectus Boron is an interesting element with unusual polymorphism. While three-dimensional (3D) structural motifs are prevalent in bulk boron, atomic boron clusters are found to have planar or quasi-planar structures, stabilized by localized two-center–two-electron (2c–2e) σ bonds on the periphery and delocalized multicenter–two-electron (nc–2e) bonds in both σ and π frameworks. Electron delocalization is a result of boron’s electron deficiency and leads to fluxional behavior, which has been observed in B13+ and B19–. A unique capability of the in-plane rotation of the inner atoms against the periphery of the cluster in a chosen direction by employing circularly polarized infrared radiation has been suggested. Such fluxional behaviors in boron clusters are interesting and have been proposed as molecular Wankel motors. The concepts of aromaticity and antiaromaticity have been extended beyond organic chemistry to planar boron clusters. The validity of these concepts in understanding the electronic structures of boron clusters is evident in the striking similarities of the π-systems of planar boron clusters to those of polycyclic aromatic hydrocarbons, such as benzene, naphthalene, coronene, anthracene, or phenanthrene. Chemical bonding models developed for boron clusters not only allowed the rationalization of the stability of boron clusters but also lead to the design of novel metal-centered boron wheels with a record-setting planar coordination number of 10. The unprecedented highly coordinated borometallic molecular wheels provide insights into the interactions between transition metals and boron and expand the frontier of boron chemistry. Another interesting feature discovered through cluster studies is boron transmutation. Even though it is well-known that B–, formed by adding one electron to boron, is isoelectronic to carbon, cluster studies have considerably expanded the possibilities of new structures and new materials using the B�

Vinayaka : A Semi-Supervised Projected Clustering Method Using Differential Evolution

OpenAIRE

Satish Gajawada; Durga Toshniwal

2012-01-01

Differential Evolution (DE) is an algorithm for evolutionary optimization. Clustering problems have beensolved by using DE based clustering methods but these methods may fail to find clusters hidden insubspaces of high dimensional datasets. Subspace and projected clustering methods have been proposed inliterature to find subspace clusters that are present in subspaces of dataset. In this paper we proposeVINAYAKA, a semi-supervised projected clustering method based on DE. In this method DE opt...

Detecting and extracting clusters in atom probe data: A simple, automated method using Voronoi cells

International Nuclear Information System (INIS)

Felfer, P.; Ceguerra, A.V.; Ringer, S.P.; Cairney, J.M.

2015-01-01

The analysis of the formation of clusters in solid solutions is one of the most common uses of atom probe tomography. Here, we present a method where we use the Voronoi tessellation of the solute atoms and its geometric dual, the Delaunay triangulation to test for spatial/chemical randomness of the solid solution as well as extracting the clusters themselves. We show how the parameters necessary for cluster extraction can be determined automatically, i.e. without user interaction, making it an ideal tool for the screening of datasets and the pre-filtering of structures for other spatial analysis techniques. Since the Voronoi volumes are closely related to atomic concentrations, the parameters resulting from this analysis can also be used for other concentration based methods such as iso-surfaces. - Highlights: • Cluster analysis of atom probe data can be significantly simplified by using the Voronoi cell volumes of the atomic distribution. • Concentration fields are defined on a single atomic basis using Voronoi cells. • All parameters for the analysis are determined by optimizing the separation probability of bulk atoms vs clustered atoms

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

In unsupervised classification, kernel -means clustering method has been shown to perform better than conventional -means clustering method in ... 518501, India; Department of Computer Science and Engineering, Jawaharlal Nehru Technological University, Anantapur College of Engineering, Anantapur 515002, India ...

Chemical pre-processing of cluster galaxies over the past 10 billion years in the IllustrisTNG simulations

Science.gov (United States)

Gupta, Anshu; Yuan, Tiantian; Torrey, Paul; Vogelsberger, Mark; Martizzi, Davide; Tran, Kim-Vy H.; Kewley, Lisa J.; Marinacci, Federico; Nelson, Dylan; Pillepich, Annalisa; Hernquist, Lars; Genel, Shy; Springel, Volker

2018-06-01

We use the IllustrisTNG simulations to investigate the evolution of the mass-metallicity relation (MZR) for star-forming cluster galaxies as a function of the formation history of their cluster host. The simulations predict an enhancement in the gas-phase metallicities of star-forming cluster galaxies (109 cluster galaxies appears prior to their infall into the central cluster potential, indicating for the first time a systematic `chemical pre-processing' signature for infalling cluster galaxies. Namely, galaxies that will fall into a cluster by z = 0 show a ˜0.05 dex enhancement in the MZR compared to field galaxies at z ≤ 0.5. Based on the inflow rate of gas into cluster galaxies and its metallicity, we identify that the accretion of pre-enriched gas is the key driver of the chemical evolution of such galaxies, particularly in the stellar mass range (109 clusters. Our results motivate future observations looking for pre-enrichment signatures in dense environments.

The smart cluster method. Adaptive earthquake cluster identification and analysis in strong seismic regions

Science.gov (United States)

Schaefer, Andreas M.; Daniell, James E.; Wenzel, Friedemann

2017-07-01

Earthquake clustering is an essential part of almost any statistical analysis of spatial and temporal properties of seismic activity. The nature of earthquake clusters and subsequent declustering of earthquake catalogues plays a crucial role in determining the magnitude-dependent earthquake return period and its respective spatial variation for probabilistic seismic hazard assessment. This study introduces the Smart Cluster Method (SCM), a new methodology to identify earthquake clusters, which uses an adaptive point process for spatio-temporal cluster identification. It utilises the magnitude-dependent spatio-temporal earthquake density to adjust the search properties, subsequently analyses the identified clusters to determine directional variation and adjusts its search space with respect to directional properties. In the case of rapid subsequent ruptures like the 1992 Landers sequence or the 2010-2011 Darfield-Christchurch sequence, a reclassification procedure is applied to disassemble subsequent ruptures using near-field searches, nearest neighbour classification and temporal splitting. The method is capable of identifying and classifying earthquake clusters in space and time. It has been tested and validated using earthquake data from California and New Zealand. A total of more than 1500 clusters have been found in both regions since 1980 with M m i n = 2.0. Utilising the knowledge of cluster classification, the method has been adjusted to provide an earthquake declustering algorithm, which has been compared to existing methods. Its performance is comparable to established methodologies. The analysis of earthquake clustering statistics lead to various new and updated correlation functions, e.g. for ratios between mainshock and strongest aftershock and general aftershock activity metrics.

The Open Cluster Chemical Abundances and Mapping (OCCAM) Survey: Optical Extension for Neutron Capture Elements

Science.gov (United States)

Melendez, Matthew; O'Connell, Julia; Frinchaboy, Peter M.; Donor, John; Cunha, Katia M. L.; Shetrone, Matthew D.; Majewski, Steven R.; Zasowski, Gail; Pinsonneault, Marc H.; Roman-Lopes, Alexandre; Stassun, Keivan G.; APOGEE Team

2017-01-01

The Open Cluster Chemical Abundance & Mapping (OCCAM) survey is a systematic survey of Galactic open clusters using data primarily from the SDSS-III/APOGEE-1 survey. However, neutron capture elements are very limited in the IR region covered by APOGEE. In an effort to fully study detailed Galactic chemical evolution, we are conducting a high resolution (R~60,000) spectroscopic abundance analysis of neutron capture elements for OCCAM clusters in the optical regime to complement the APOGEE results. As part of this effort, we present Ba II, La II, Ce II and Eu II results for a few open clusters without previous abundance measurements using data obtained at McDonald Observatory with the 2.1m Otto Struve telescope and Sandiford Echelle Spectrograph.This work is supported by an NSF AAG grant AST-1311835.

Chemical Bonding of Transition-Metal Co13 Clusters with Graphene.

Science.gov (United States)

Alonso-Lanza, Tomás; Ayuela, Andrés; Aguilera-Granja, Faustino

2015-12-01

We carried out density functional calculations to study the adsorption of Co13 clusters on graphene. Several free isomers were deposited at different positions with respect to the hexagonal lattice nodes, allowing us to study even the hcp 2d isomer, which was recently obtained as the most stable one. Surprisingly, the Co13 clusters attached to graphene prefer icosahedron-like structures in which the low-lying isomer is much distorted; in such structures, they are linked with more bonds than those reported in previous works. For any isomer, the most stable position binds to graphene by the Co atoms that can lose electrons. We find that the charge transfer between graphene and the clusters is small enough to conclude that the Co-graphene binding is not ionic-like but chemical. Besides, the same order of stability among the different isomers on doped graphene is kept. These findings could also be of interest for magnetic clusters on graphenic nanostructures such as ribbons and nanotubes. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Chemical abundances of globular clusters in NGC 5128 (Centaurus A)

Science.gov (United States)

Hernandez, Svea; Larsen, Søren; Trager, Scott; Kaper, Lex; Groot, Paul

2018-06-01

We perform a detailed abundance analysis on integrated-light spectra of 20 globular clusters (GCs) in the early-type galaxy NGC 5128 (Centaurus A). The GCs were observed with X-Shooter on the Very Large Telescope (VLT). The cluster sample spans a metallicity range of -1.92 poor GCs in NGC 5128 is genuine, it could hint at a chemical enrichment history different than that experienced by the MW. We also measure Na abundances in 9 out of 20 GCs. We find evidence for intracluster abundance variations in six of these clusters where we see enhanced [Na/Fe] > +0.25 dex. We obtain the first abundance measurements of Cr, Mn, and Ni for a sample of the GC population in NGC 5128 and find consistency with the overall trends observed in the MW, with a slight enhancement (<0.1 dex) in the Fe-peak abundances measured in the NGC 5128.

A cluster-based strategy for assessing the overlap between large chemical libraries and its application to a recent acquisition.

Science.gov (United States)

Engels, Michael F M; Gibbs, Alan C; Jaeger, Edward P; Verbinnen, Danny; Lobanov, Victor S; Agrafiotis, Dimitris K

2006-01-01

We report on the structural comparison of the corporate collections of Johnson & Johnson Pharmaceutical Research & Development (JNJPRD) and 3-Dimensional Pharmaceuticals (3DP), performed in the context of the recent acquisition of 3DP by JNJPRD. The main objective of the study was to assess the druglikeness of the 3DP library and the extent to which it enriched the chemical diversity of the JNJPRD corporate collection. The two databases, at the time of acquisition, collectively contained more than 1.1 million compounds with a clearly defined structural description. The analysis was based on a clustering approach and aimed at providing an intuitive quantitative estimate and visual representation of this enrichment. A novel hierarchical clustering algorithm called divisive k-means was employed in combination with Kelley's cluster-level selection method to partition the combined data set into clusters, and the diversity contribution of each library was evaluated as a function of the relative occupancy of these clusters. Typical 3DP chemotypes enriching the diversity of the JNJPRD collection were catalogued and visualized using a modified maximum common substructure algorithm. The joint collection of JNJPRD and 3DP compounds was also compared to other databases of known medicinally active or druglike compounds. The potential of the methodology for the analysis of very large chemical databases is discussed.

DETAILED CHEMICAL ABUNDANCES OF FOUR STARS IN THE UNUSUAL GLOBULAR CLUSTER PALOMAR 1

International Nuclear Information System (INIS)

Sakari, Charli M.; Venn, Kim A.; Irwin, Mike; Aoki, Wako; Arimoto, Nobuo; Dotter, Aaron

2011-01-01

Detailed chemical abundances for 21 elements are presented for four red giants in the anomalous outer halo globular cluster Palomar 1 (R GC = 17.2 kpc, Z = 3.6 kpc) using high-resolution (R = 36, 000) spectra from the High Dispersion Spectrograph on the Subaru Telescope. Pal 1 has long been considered unusual because of its low surface brightness, sparse red giant branch, young age, and its possible association with two extragalactic streams of stars. This paper shows that its chemistry further confirms its unusual nature. The mean metallicity of the four stars, [Fe/H] = -0.60 ± 0.01, is high for a globular cluster so far from the Galactic center, but is low for a typical open cluster. The [α/Fe] ratios, though in agreement with the Galactic stars within the 1σ errors, agree best with the lower values in dwarf galaxies. No signs of the Na/O anticorrelation are detected in Pal 1, though Na appears to be marginally high in all four stars. Pal 1's neutron-capture elements are also unusual: its high [Ba/Y] ratio agrees best with dwarf galaxies, implying an excess of second-peak over first-peak s-process elements, while its [Eu/α] and [Ba/Eu] ratios show that Pal 1's contributions from the r-process must have differed in some way from normal Galactic stars. Therefore, Pal 1 is unusual chemically, as well in its other properties. Pal 1 shares some of its unusual abundance characteristics with the young clusters associated with the Sagittarius dwarf galaxy remnant and the intermediate-age LMC clusters, and could be chemically associated with the Canis Majoris overdensity; however, it does not seem to be similar to the Monoceros/Galactic Anticenter Stellar Stream.

Shaping a novel security approach in chemical industrial clusters to prevent large-scale domino events

NARCIS (Netherlands)

Reniers, Genserik L L; Dullaert, Wout; Soudan, Karel

2009-01-01

Two aspects are important when it comes to guaranteeing an effective and efficient security policy in a chemical industrial cluster. The first issue involves obtaining an acceptable level of collaboration between the different enterprises forming the cluster. The second topic is to ensure that an

THE BIZARRE CHEMICAL INVENTORY OF NGC 2419, AN EXTREME OUTER HALO GLOBULAR CLUSTER

Energy Technology Data Exchange (ETDEWEB)

Cohen, Judith G.; Kirby, Evan N., E-mail: jlc@astro.caltech.edu, E-mail: enk@astro.caltech.edu [Palomar Observatory, Mail Stop 249-17, California Institute of Technology, Pasadena, CA 91125 (United States)

2012-11-20

We present new Keck/HIRES observations of six red giants in the globular cluster (GC) NGC 2419. Although the cluster is among the most distant and most luminous in the Milky Way, it was considered chemically ordinary until very recently. Our previous work showed that the near-infrared Ca II triplet line strength varied more than expected for a chemically homogeneous cluster, and that at least one star had unusual abundances of Mg and K. Here, we confirm that NGC 2419 harbors a population of stars, comprising about one-third of its mass, that is depleted in Mg by a factor of eight and enhanced in K by a factor of six with respect to the Mg-normal population. Although the majority, Mg-normal population appears to have a chemical abundance pattern indistinguishable from ordinary, inner-halo GCs, the Mg-poor population exhibits dispersions of several elements. The abundances of K and Sc are strongly anti-correlated with Mg, and some other elements (Si and Ca among others) are weakly anti-correlated with Mg. These abundance patterns suggest that the different populations of NGC 2419 sample the ejecta of diverse supernovae in addition to asymptotic giant branch ejecta. However, the abundances of Fe-peak elements except Sc show no star-to-star variation. We find no nucleosynthetic source that satisfactorily explains all of the abundance variations in this cluster. Because NGC 2419 appears like no other GC, we reiterate our previous suggestion that it is not a GC at all, but rather the core of an accreted dwarf galaxy.

A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method

Directory of Open Access Journals (Sweden)

Xiangbing Zhou

2018-04-01

Full Text Available Rapidly growing GPS (Global Positioning System trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM, our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

An Examination of Three Spatial Event Cluster Detection Methods

Directory of Open Access Journals (Sweden)

Hensley H. Mariathas

2015-03-01

Full Text Available In spatial disease surveillance, geographic areas with large numbers of disease cases are to be identified, so that targeted investigations can be pursued. Geographic areas with high disease rates are called disease clusters and statistical cluster detection tests are used to identify geographic areas with higher disease rates than expected by chance alone. In some situations, disease-related events rather than individuals are of interest for geographical surveillance, and methods to detect clusters of disease-related events are called event cluster detection methods. In this paper, we examine three distributional assumptions for the events in cluster detection: compound Poisson, approximate normal and multiple hypergeometric (exact. The methods differ on the choice of distributional assumption for the potentially multiple correlated events per individual. The methods are illustrated on emergency department (ED presentations by children and youth (age < 18 years because of substance use in the province of Alberta, Canada, during 1 April 2007, to 31 March 2008. Simulation studies are conducted to investigate Type I error and the power of the clustering methods.

Chemical analysis of eight giant stars of the globular cluster NGC 6366

Science.gov (United States)

Puls, Arthur A.; Alves-Brito, Alan; Campos, Fabíola; Dias, Bruno; Barbuy, Beatriz

2018-05-01

The metal-rich Galactic globular cluster NGC 6366 is the fifth closest to the Sun. Despite its interest, it has received scarce attention, and little is known about its internal structure. Its kinematics suggests a link to the halo, but its metallicity indicates otherwise. We present a detailed chemical analysis of eight giant stars of NGC 6366, using high-resolution and high-quality spectra (R > 40 000, S/N > 60) obtained at the VLT (8.2 m) and CFHT (3.6 m) telescopes. We attempted to characterize its chemistry and to search for evidence of multiple stellar populations. The atmospheric parameters were derived using the method of excitation and ionization equilibrium of Fe I and Fe II lines and from those atmospheric parameters we calculated the abundances for other elements and found that none of the elements measured presents star-to-star variation greater than the uncertainties. We compared the derived abundances with those of other globular clusters and field stars available in the literature. We determined a mean [Fe/H] = -0.60 ± 0.03 for NGC 6366 and found some similarity of this object with M 71, another inner halo globular cluster. The Na-O anticorrelation extension is short and no star-to-star variation in Al is found. The presence of second generation stars is not evident in NGC 6366.

MANNER OF STOCKS SORTING USING CLUSTER ANALYSIS METHODS

Directory of Open Access Journals (Sweden)

Jana Halčinová

2014-06-01

Full Text Available The aim of the present article is to show the possibility of using the methods of cluster analysis in classification of stocks of finished products. Cluster analysis creates groups (clusters of finished products according to similarity in demand i.e. customer requirements for each product. Manner stocks sorting of finished products by clusters is described a practical example. The resultants clusters are incorporated into the draft layout of the distribution warehouse.

Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign

Directory of Open Access Journals (Sweden)

Maciej Kutera

2010-10-01

Full Text Available Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four selected methods which have been chosen because their peculiarities suggested their applicability to our purposes. One of the analyzed method k-means clustering with random selected initial cluster seeds gave very good results in customer segmentation to manage advertisement campaign and these results were presented in details in the article. In contrast one of the methods (hierarchical average linkage was found useless in customer segmentation. Further investigations concerning benefits of clustering methods in customer segmentation to manage advertisement campaign is worth continuing, particularly that finding solutions in this field can give measurable profits for marketing activity.

Comparing the performance of biomedical clustering methods

DEFF Research Database (Denmark)

Wiwie, Christian; Baumbach, Jan; Röttger, Richard

2015-01-01

expression to protein domains. Performance was judged on the basis of 13 common cluster validity indices. We developed a clustering analysis platform, ClustEval (http://clusteval.mpi-inf.mpg.de), to promote streamlined evaluation, comparison and reproducibility of clustering results in the future......Identifying groups of similar objects is a popular first step in biomedical data analysis, but it is error-prone and impossible to perform manually. Many computational methods have been developed to tackle this problem. Here we assessed 13 well-known methods using 24 data sets ranging from gene....... This allowed us to objectively evaluate the performance of all tools on all data sets with up to 1,000 different parameter sets each, resulting in a total of more than 4 million calculated cluster validity indices. We observed that there was no universal best performer, but on the basis of this wide...

An efficient laser vaporization source for chemically modified metal clusters characterized by thermodynamics and kinetics

Science.gov (United States)

Masubuchi, Tsugunosuke; Eckhard, Jan F.; Lange, Kathrin; Visser, Bradley; Tschurl, Martin; Heiz, Ulrich

2018-02-01

A laser vaporization cluster source that has a room for cluster aggregation and a reactor volume, each equipped with a pulsed valve, is presented for the efficient gas-phase production of chemically modified metal clusters. The performance of the cluster source is evaluated through the production of Ta and Ta oxide cluster cations, TaxOy+ (y ≥ 0). It is demonstrated that the cluster source produces TaxOy+ over a wide mass range, the metal-to-oxygen ratio of which can easily be controlled by changing the pulse duration that influences the amount of reactant O2 introduced into the cluster source. Reaction kinetic modeling shows that the generation of the oxides takes place under thermalized conditions at less than 300 K, whereas metal cluster cores are presumably created with excess heat. These characteristics are also advantageous to yield "reaction intermediates" of interest via reactions between clusters and reactive molecules in the cluster source, which may subsequently be mass selected for their reactivity measurements.

Comparing Chemical Compositions of Dwarf Elliptical Galaxies and Globular Clusters

Science.gov (United States)

Chu, Jason; Sparkman, Lea; Toloba, Elisa; Guhathakurta, Puragra

2015-01-01

Because of their abundance in cluster environments and fragility due to their low mass, dwarf elliptical galaxies (dEs) are excellent specimens for studying the physical processes that occur inside galaxy clusters. These studies can be used to expand our understanding of the process of galaxy (specifically dE) formation and the role of dark matter in the Universe. To move closer to better understanding these topics, we present a study of the relationship between dEs and globular clusters (GCs) by using the largest sample of dEs and GC satellites to date. We focus on comparing the ages and chemical compositions of dE nuclei with those of satellite GCs by analyzing absorption lines in their spectra. To better view the spectral features of these relatively dim objects, we employ a spectral co-addition process, where we add the fluxes of several objects to produce a single spectrum with high signal-to-noise ratio. Our finding that dE nuclei are younger and more metal rich than globular clusters establishes important benchmarks that future dE formation theories will consider. We also establish a means to identify GCs whose parent galaxies are uncertain, which allows us to make comparisons between this GC group and the satellite GCs.

METHOD OF CONSTRUCTION OF GENETIC DATA CLUSTERS

Directory of Open Access Journals (Sweden)

N. A. Novoselova

2016-01-01

Full Text Available The paper presents a method of construction of genetic data clusters (functional modules using the randomized matrices. To build the functional modules the selection and analysis of the eigenvalues of the gene profiles correlation matrix is performed. The principal components, corresponding to the eigenvalues, which are significantly different from those obtained for the randomly generated correlation matrix, are used for the analysis. Each selected principal component forms gene cluster. In a comparative experiment with the analogs the proposed method shows the advantage in allocating statistically significant different-sized clusters, the ability to filter non- informative genes and to extract the biologically interpretable functional modules matching the real data structure.

Domino effects within a chemical cluster : A game-theoretical modeling approach by using Nash-equilibrium

NARCIS (Netherlands)

Reniers, Genserik; Dullaert, Wout; Karel, Soudan

2009-01-01

Every company situated within a chemical cluster faces domino effect risks, whose magnitude depends on every company's own risk management strategies and on those of all others. Preventing domino effects is therefore very important to avoid catastrophes in the chemical process industry. Given that

Clustering Methods Application for Customer Segmentation to Manage Advertisement Campaign

OpenAIRE

Maciej Kutera; Mirosława Lasek

2010-01-01

Clustering methods are recently so advanced elaborated algorithms for large collection data analysis that they have been already included today to data mining methods. Clustering methods are nowadays larger and larger group of methods, very quickly evolving and having more and more various applications. In the article, our research concerning usefulness of clustering methods in customer segmentation to manage advertisement campaign is presented. We introduce results obtained by using four sel...

The chemical composition of a regular halo globular cluster: NGC 5897

Science.gov (United States)

Koch, Andreas; McWilliam, Andrew

2014-05-01

We report for the first time on the chemical composition of the halo cluster NGC 5897 (R⊙ = 12.5 kpc), based on chemical abundance ratios for 27 α-, iron-peak, and neutron-capture elements in seven red giants. From our high-resolution, high signal-to-noise spectra obtained with the Magellan/MIKE spectrograph, we find a mean iron abundance from the neutral species of [Fe/H] = - 2.04 ± 0.01 (stat.) ± 0.15 (sys.), which is more metal-poor than implied by previous photometric and low-resolution spectroscopic studies. The cluster NGC 5897 is α-enhanced (to 0.34 ± 0.01 dex) and shows Fe-peak element ratios typical of other (metal-poor) halo globular clusters (GCs) with no overall, significant abundance spreads in iron or in any other heavy element. Like other GCs, NGC 5897 shows a clear Na-O anti-correlation, where we find a prominent primordial population of stars with enhanced O abundances and approximately solar Na/Fe ratios, while two stars are Na-rich, providing chemical proof of the presence of multiple populations in this cluster. Comparison of the heavy element abundances with the solar-scaled values and the metal-poor GC M15 from the literature confirms that NGC 5897 has experienced little contribution from s-process nucleosynthesis. One star of the first generation stands out in that it shows very low La and Eu abundances. Overall, NGC 5897 is a well behaved GC showing archetypical correlations and element-patterns, with little room for surprises in our data. We suggest that its lower metallicity could explain the unusually long periods of RR Lyr that were found in NGC 5897. This paper includes data gathered with the 6.5-m Magellan Telescopes located at Las Campanas Observatory, Chile.Table 5 is available in electronic form at http://www.aanda.orgFull Table 2 is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/565/A23

Insight into acid-base nucleation experiments by comparison of the chemical composition of positive, negative, and neutral clusters.

Science.gov (United States)

Bianchi, Federico; Praplan, Arnaud P; Sarnela, Nina; Dommen, Josef; Kürten, Andreas; Ortega, Ismael K; Schobesberger, Siegfried; Junninen, Heikki; Simon, Mario; Tröstl, Jasmin; Jokinen, Tuija; Sipilä, Mikko; Adamov, Alexey; Amorim, Antonio; Almeida, Joao; Breitenlechner, Martin; Duplissy, Jonathan; Ehrhart, Sebastian; Flagan, Richard C; Franchin, Alessandro; Hakala, Jani; Hansel, Armin; Heinritzi, Martin; Kangasluoma, Juha; Keskinen, Helmi; Kim, Jaeseok; Kirkby, Jasper; Laaksonen, Ari; Lawler, Michael J; Lehtipalo, Katrianne; Leiminger, Markus; Makhmutov, Vladimir; Mathot, Serge; Onnela, Antti; Petäjä, Tuukka; Riccobono, Francesco; Rissanen, Matti P; Rondo, Linda; Tomé, António; Virtanen, Annele; Viisanen, Yrjö; Williamson, Christina; Wimmer, Daniela; Winkler, Paul M; Ye, Penglin; Curtius, Joachim; Kulmala, Markku; Worsnop, Douglas R; Donahue, Neil M; Baltensperger, Urs

2014-12-02

We investigated the nucleation of sulfuric acid together with two bases (ammonia and dimethylamine), at the CLOUD chamber at CERN. The chemical composition of positive, negative, and neutral clusters was studied using three Atmospheric Pressure interface-Time Of Flight (APi-TOF) mass spectrometers: two were operated in positive and negative mode to detect the chamber ions, while the third was equipped with a nitrate ion chemical ionization source allowing detection of neutral clusters. Taking into account the possible fragmentation that can happen during the charging of the ions or within the first stage of the mass spectrometer, the cluster formation proceeded via essentially one-to-one acid-base addition for all of the clusters, independent of the type of the base. For the positive clusters, the charge is carried by one excess protonated base, while for the negative clusters it is carried by a deprotonated acid; the same is true for the neutral clusters after these have been ionized. During the experiments involving sulfuric acid and dimethylamine, it was possible to study the appearance time for all the clusters (positive, negative, and neutral). It appeared that, after the formation of the clusters containing three molecules of sulfuric acid, the clusters grow at a similar speed, independent of their charge. The growth rate is then probably limited by the arrival rate of sulfuric acid or cluster-cluster collision.

Cluster chemical ionization for improved confidence level in sample identification by gas chromatography/mass spectrometry.

Science.gov (United States)

Fialkov, Alexander B; Amirav, Aviv

2003-01-01

Upon the supersonic expansion of helium mixed with vapor from an organic solvent (e.g. methanol), various clusters of the solvent with the sample molecules can be formed. As a result of 70 eV electron ionization of these clusters, cluster chemical ionization (cluster CI) mass spectra are obtained. These spectra are characterized by the combination of EI mass spectra of vibrationally cold molecules in the supersonic molecular beam (cold EI) with CI-like appearance of abundant protonated molecules, together with satellite peaks corresponding to protonated or non-protonated clusters of sample compounds with 1-3 solvent molecules. Like CI, cluster CI preferably occurs for polar compounds with high proton affinity. However, in contrast to conventional CI, for non-polar compounds or those with reduced proton affinity the cluster CI mass spectrum converges to that of cold EI. The appearance of a protonated molecule and its solvent cluster peaks, plus the lack of protonation and cluster satellites for prominent EI fragments, enable the unambiguous identification of the molecular ion. In turn, the insertion of the proper molecular ion into the NIST library search of the cold EI mass spectra eliminates those candidates with incorrect molecular mass and thus significantly increases the confidence level in sample identification. Furthermore, molecular mass identification is of prime importance for the analysis of unknown compounds that are absent in the library. Examples are given with emphasis on the cluster CI analysis of carbamate pesticides, high explosives and unknown samples, to demonstrate the usefulness of Supersonic GC/MS (GC/MS with supersonic molecular beam) in the analysis of these thermally labile compounds. Cluster CI is shown to be a practical ionization method, due to its ease-of-use and fast instrumental conversion between EI and cluster CI, which involves the opening of only one valve located at the make-up gas path. The ease-of-use of cluster CI is analogous

Method for detecting clusters of possible uranium deposits

International Nuclear Information System (INIS)

Conover, W.J.; Bement, T.R.; Iman, R.L.

1978-01-01

When a two-dimensional map contains points that appear to be scattered somewhat at random, a question that often arises is whether groups of points that appear to cluster are merely exhibiting ordinary behavior, which one can expect with any random distribution of points, or whether the clusters are too pronounced to be attributable to chance alone. A method for detecting clusters along a straight line is applied to the two-dimensional map of 214 Bi anomalies observed as part of the National Uranium Resource Evaluation Program in the Lubbock, Texas, region. Some exact probabilities associated with this method are computed and compared with two approximate methods. The two methods for approximating probabilities work well in the cases examined and can be used when it is not feasible to obtain the exact probabilities

Clustering of samples and elements based on multi-variable chemical data

International Nuclear Information System (INIS)

Op de Beeck, J.

1984-01-01

Clustering and classification are defined in the context of multivariable chemical analysis data. Classical multi-variate techniques, commonly used to interpret such data, are shown to be based on probabilistic and geometrical principles which are not justified for analytical data, since in that case one assumes or expects a system of more or less systematically related objects (samples) as defined by measurements on more or less systematically interdependent variables (elements). For the specific analytical problem of data set concerning a large number of trace elements determined in a large number of samples, a deterministic cluster analysis can be used to develop the underlying classification structure. Three main steps can be distinguished: diagnostic evaluation and preprocessing of the raw input data; computation of a symmetric matrix with pairwise standardized dissimilarity values between all possible pairs of samples and/or elements; and ultrametric clustering strategy to produce the final classification as a dendrogram. The software packages designed to perform these tasks are discussed and final results are given. Conclusions are formulated concerning the dangers of using multivariate, clustering and classification software packages as a black-box

A Web service substitution method based on service cluster nets

Science.gov (United States)

Du, YuYue; Gai, JunJing; Zhou, MengChu

2017-11-01

Service substitution is an important research topic in the fields of Web services and service-oriented computing. This work presents a novel method to analyse and substitute Web services. A new concept, called a Service Cluster Net Unit, is proposed based on Web service clusters. A service cluster is converted into a Service Cluster Net Unit. Then it is used to analyse whether the services in the cluster can satisfy some service requests. Meanwhile, the substitution methods of an atomic service and a composite service are proposed. The correctness of the proposed method is proved, and the effectiveness is shown and compared with the state-of-the-art method via an experiment. It can be readily applied to e-commerce service substitution to meet the business automation needs.

The relationship between supplier networks and industrial clusters: an analysis based on the cluster mapping method

Directory of Open Access Journals (Sweden)

Ichiro IWASAKI

2010-06-01

Full Text Available Michael Porter’s concept of competitive advantages emphasizes the importance of regional cooperation of various actors in order to gain competitiveness on globalized markets. Foreign investors may play an important role in forming such cooperation networks. Their local suppliers tend to concentrate regionally. They can form, together with local institutions of education, research, financial and other services, development agencies, the nucleus of cooperative clusters. This paper deals with the relationship between supplier networks and clusters. Two main issues are discussed in more detail: the interest of multinational companies in entering regional clusters and the spillover effects that may stem from their participation. After the discussion on the theoretical background, the paper introduces a relatively new analytical method: “cluster mapping” - a method that can spot regional hot spots of specific economic activities with cluster building potential. Experience with the method was gathered in the US and in the European Union. After the discussion on the existing empirical evidence, the authors introduce their own cluster mapping results, which they obtained by using a refined version of the original methodology.

Single pass kernel k-means clustering method

Indian Academy of Sciences (India)

paper proposes a simple and faster version of the kernel k-means clustering ... It has been considered as an important tool ... On the other hand, kernel-based clustering methods, like kernel k-means clus- ..... able at the UCI machine learning repository (Murphy 1994). ... All the data sets have only numeric valued features.

Clustering Methods with Qualitative Data: a Mixed-Methods Approach for Prevention Research with Small Samples.

Science.gov (United States)

Henry, David; Dymnicki, Allison B; Mohatt, Nathaniel; Allen, James; Kelly, James G

2015-10-01

Qualitative methods potentially add depth to prevention research but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed-methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed-methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-means clustering, and latent class analysis produced similar levels of accuracy with binary data and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a "real-world" example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities.

Clustering Methods with Qualitative Data: A Mixed Methods Approach for Prevention Research with Small Samples

Science.gov (United States)

Henry, David; Dymnicki, Allison B.; Mohatt, Nathaniel; Allen, James; Kelly, James G.

2016-01-01

Qualitative methods potentially add depth to prevention research, but can produce large amounts of complex data even with small samples. Studies conducted with culturally distinct samples often produce voluminous qualitative data, but may lack sufficient sample sizes for sophisticated quantitative analysis. Currently lacking in mixed methods research are methods allowing for more fully integrating qualitative and quantitative analysis techniques. Cluster analysis can be applied to coded qualitative data to clarify the findings of prevention studies by aiding efforts to reveal such things as the motives of participants for their actions and the reasons behind counterintuitive findings. By clustering groups of participants with similar profiles of codes in a quantitative analysis, cluster analysis can serve as a key component in mixed methods research. This article reports two studies. In the first study, we conduct simulations to test the accuracy of cluster assignment using three different clustering methods with binary data as produced when coding qualitative interviews. Results indicated that hierarchical clustering, K-Means clustering, and latent class analysis produced similar levels of accuracy with binary data, and that the accuracy of these methods did not decrease with samples as small as 50. Whereas the first study explores the feasibility of using common clustering methods with binary data, the second study provides a “real-world” example using data from a qualitative study of community leadership connected with a drug abuse prevention project. We discuss the implications of this approach for conducting prevention research, especially with small samples and culturally distinct communities. PMID:25946969

Electronic and chemical properties of barium and indium clusters

International Nuclear Information System (INIS)

Onwuagba, B.N.

1992-11-01

The ground state electronic and chemical properties of divalent barium and trivalent indium are investigated in a self-consistent manner using the spin-polarized local density approximation in the framework of Density Functional Theory. A jellium model is adopted in the spirit of Gunnarsson and Lundqvist exchange and correlation energies and the calculated properties primarily associated with the s-p orbitals in barium and p orbitals in indium provide deepened insight towards the understanding of the mechanisms to the magic numbers in both clusters. (author). 21 refs, 5 figs

The polarizable embedding coupled cluster method

DEFF Research Database (Denmark)

Sneskov, Kristian; Schwabe, Tobias; Kongsted, Jacob

2011-01-01

We formulate a new combined quantum mechanics/molecular mechanics (QM/MM) method based on a self-consistent polarizable embedding (PE) scheme. For the description of the QM region, we apply the popular coupled cluster (CC) method detailing the inclusion of electrostatic and polarization effects...

Coupling microscopic and mesoscopic scales to simulate chemical equilibrium between a nanometric carbon cluster and detonation products fluid.

Science.gov (United States)

Bourasseau, Emeric; Maillet, Jean-Bernard

2011-04-21

This paper presents a new method to obtain chemical equilibrium properties of detonation products mixtures including a solid carbon phase. In this work, the solid phase is modelled through a mesoparticle immersed in the fluid, such that the heterogeneous character of the mixture is explicitly taken into account. Inner properties of the clusters are taken from an equation of state obtained in a previous work, and interaction potential between the nanocluster and the fluid particles is derived from all-atoms simulations using the LCBOPII potential (Long range Carbon Bond Order Potential II). It appears that differences in chemical equilibrium results obtained with this method and the "composite ensemble method" (A. Hervouet et al., J. Phys. Chem. B, 2008, 112.), where fluid and solid phases are considered as non-interacting, are not significant, underlining the fact that considering the inhomogeneity of such system is crucial.

Atomic and electronic structure of clusters from car-Parrinello method

International Nuclear Information System (INIS)

Kumar, V.

1994-06-01

With the development of ab-initio molecular dynamics method, it has now become possible to study the static and dynamical properties of clusters containing up to a few tens of atoms. Here I present a review of the method within the framework of the density functional theory and pseudopotential approach to represent the electron-ion interaction and discuss some of its applications to clusters. Particular attention is focussed on the structure and bonding properties of clusters as a function of their size. Applications to clusters of alkali metals and Al, non-metal - metal transition in divalent metal clusters, molecular clusters of carbon and Sb are discussed in detail. Some results are also presented on mixed clusters. (author). 121 refs, 24 ifigs

Performance Analysis of Entropy Methods on K Means in Clustering Process

Science.gov (United States)

Dicky Syahputra Lubis, Mhd.; Mawengkang, Herman; Suwilo, Saib

2017-12-01

K Means is a non-hierarchical data clustering method that attempts to partition existing data into one or more clusters / groups. This method partitions the data into clusters / groups so that data that have the same characteristics are grouped into the same cluster and data that have different characteristics are grouped into other groups.The purpose of this data clustering is to minimize the objective function set in the clustering process, which generally attempts to minimize variation within a cluster and maximize the variation between clusters. However, the main disadvantage of this method is that the number k is often not known before. Furthermore, a randomly chosen starting point may cause two points to approach the distance to be determined as two centroids. Therefore, for the determination of the starting point in K Means used entropy method where this method is a method that can be used to determine a weight and take a decision from a set of alternatives. Entropy is able to investigate the harmony in discrimination among a multitude of data sets. Using Entropy criteria with the highest value variations will get the highest weight. Given this entropy method can help K Means work process in determining the starting point which is usually determined at random. Thus the process of clustering on K Means can be more quickly known by helping the entropy method where the iteration process is faster than the K Means Standard process. Where the postoperative patient dataset of the UCI Repository Machine Learning used and using only 12 data as an example of its calculations is obtained by entropy method only with 2 times iteration can get the desired end result.

A Clustering Method for Data in Cylindrical Coordinates

Directory of Open Access Journals (Sweden)

Kazuhisa Fujita

2017-01-01

Full Text Available We propose a new clustering method for data in cylindrical coordinates based on the k-means. The goal of the k-means family is to maximize an optimization function, which requires a similarity. Thus, we need a new similarity to obtain the new clustering method for data in cylindrical coordinates. In this study, we first derive a new similarity for the new clustering method by assuming a particular probabilistic model. A data point in cylindrical coordinates has radius, azimuth, and height. We assume that the azimuth is sampled from a von Mises distribution and the radius and the height are independently generated from isotropic Gaussian distributions. We derive the new similarity from the log likelihood of the assumed probability distribution. Our experiments demonstrate that the proposed method using the new similarity can appropriately partition synthetic data defined in cylindrical coordinates. Furthermore, we apply the proposed method to color image quantization and show that the methods successfully quantize a color image with respect to the hue element.

A cluster merging method for time series microarray with production values.

Science.gov (United States)

Chira, Camelia; Sedano, Javier; Camara, Monica; Prieto, Carlos; Villar, Jose R; Corchado, Emilio

2014-09-01

A challenging task in time-course microarray data analysis is to cluster genes meaningfully combining the information provided by multiple replicates covering the same key time points. This paper proposes a novel cluster merging method to accomplish this goal obtaining groups with highly correlated genes. The main idea behind the proposed method is to generate a clustering starting from groups created based on individual temporal series (representing different biological replicates measured in the same time points) and merging them by taking into account the frequency by which two genes are assembled together in each clustering. The gene groups at the level of individual time series are generated using several shape-based clustering methods. This study is focused on a real-world time series microarray task with the aim to find co-expressed genes related to the production and growth of a certain bacteria. The shape-based clustering methods used at the level of individual time series rely on identifying similar gene expression patterns over time which, in some models, are further matched to the pattern of production/growth. The proposed cluster merging method is able to produce meaningful gene groups which can be naturally ranked by the level of agreement on the clustering among individual time series. The list of clusters and genes is further sorted based on the information correlation coefficient and new problem-specific relevant measures. Computational experiments and results of the cluster merging method are analyzed from a biological perspective and further compared with the clustering generated based on the mean value of time series and the same shape-based algorithm.

Neutron-Capture Nucleosynthesis and the Chemical Evolution of Globular Clusters

Science.gov (United States)

Shingles, Luke J.

2015-09-01

Elements heavier than iron are almost entirely produced in stars through neutron captures and radioactive decays. Of these heavy elements, roughly half are produced by the slow neutron-capture process (s-process), which takes place under extended exposure to low neutron densities. Most of the s-process production occurs in stars with initial masses between roughly 0.8 and 8 solar masses (Msun), which evolve through the Asymptotic Giant Branch (AGB) phase. This thesis explores several topics related to AGB stars and the s-process, with a focus on comparing theoretical models to observations in the literature on planetary nebulae, post-AGB stars, and globular cluster stars. A recurring theme is the uncertainty of carbon-13-pocket formation, which is crucial for building accurate models of s-process nucleosynthesis. We first investigated whether neutron-capture reactions in AGB stars are the cause of the low sulphur abundances in planetary nebulae and post-AGB stars relative to the interstellar medium. Accounting for uncertainties in the size of the partial mixing zone that forms carbon-13 pockets and the rates of neutron-capture and neutron-producing reactions, our models failed to reproduce the observed levels of sulphur destruction. From this, we concluded that AGB nucleosynthesis is not the cause of the sulphur anomaly. We also discovered a new method to constrain the extent of the partial mixing zone using neon abundances in planetary nebulae. We next aimed to discover the stellar sites of the s-process enrichment in globular clusters that have inter- and intra-cluster variation, with the examples of M4 (relative to M5) and M22, respectively. Using a new chemical evolution code developed by the candidate, we tested models with stellar yields from rotating massive stars and AGB stars. We compared our model predictions for the production of s-process elements with abundances from s-poor and s-rich populations. We found that rotating massive stars alone do not

The Local Maximum Clustering Method and Its Application in Microarray Gene Expression Data Analysis

Directory of Open Access Journals (Sweden)

Chen Yidong

2004-01-01

Full Text Available An unsupervised data clustering method, called the local maximum clustering (LMC method, is proposed for identifying clusters in experiment data sets based on research interest. A magnitude property is defined according to research purposes, and data sets are clustered around each local maximum of the magnitude property. By properly defining a magnitude property, this method can overcome many difficulties in microarray data clustering such as reduced projection in similarities, noises, and arbitrary gene distribution. To critically evaluate the performance of this clustering method in comparison with other methods, we designed three model data sets with known cluster distributions and applied the LMC method as well as the hierarchic clustering method, the -mean clustering method, and the self-organized map method to these model data sets. The results show that the LMC method produces the most accurate clustering results. As an example of application, we applied the method to cluster the leukemia samples reported in the microarray study of Golub et al. (1999.

The Open Cluster Chemical Abundances and Mapping (OCCAM) Survey: Galactic Neutron CaptureAbundance Gradients

Science.gov (United States)

O'Connell, Julia; Frinchaboy, Peter M.; Shetrone, Matthew D.; Melendez, Matthew; Cunha, Katia; Majewski, Steven R.; Zasowski, Gail; APOGEE Team

2017-06-01

The evolution of elements, as a function or age, throughout the Milky Way disk provides a key constraint for galaxy evolution models. In an effort to provide these constraints, we have conducted an investigation into the r- and s- process elemental abundances for a large sample of open clusters as part of an optical follow-up to the SDSS-III/APOGEE-1 survey. Stars were identified as cluster members by the Open Cluster Chemical Abundance & Mapping (OCCAM) survey, which culls member candidates by radial velocity, metallicity and proper motion from the observed APOGEE sample. To obtain data for neutron capture elements in these clusters, we conducted a long-term observing campaign covering three years (2013-2016) using the McDonald Observatory Otto Struve 2.1-m telescope and Sandiford Cass Echelle Spectrograph (R ~ 60,000). We present Galactic neutron capture abundance gradients using 30+ clusters, within 6 kpc of the Sun, covering a range of ages from ~80 Myr to ~10 Gyr .

Computational studies of atmospherically-relevant chemical reactions in water clusters and on liquid water and ice surfaces.

Science.gov (United States)

Gerber, R Benny; Varner, Mychel E; Hammerich, Audrey D; Riikonen, Sampsa; Murdachaew, Garold; Shemesh, Dorit; Finlayson-Pitts, Barbara J

2015-02-17

CONSPECTUS: Reactions on water and ice surfaces and in other aqueous media are ubiquitous in the atmosphere, but the microscopic mechanisms of most of these processes are as yet unknown. This Account examines recent progress in atomistic simulations of such reactions and the insights provided into mechanisms and interpretation of experiments. Illustrative examples are discussed. The main computational approaches employed are classical trajectory simulations using interaction potentials derived from quantum chemical methods. This comprises both ab initio molecular dynamics (AIMD) and semiempirical molecular dynamics (SEMD), the latter referring to semiempirical quantum chemical methods. Presented examples are as follows: (i) Reaction of the (NO(+))(NO3(-)) ion pair with a water cluster to produce the atmospherically important HONO and HNO3. The simulations show that a cluster with four water molecules describes the reaction. This provides a hydrogen-bonding network supporting the transition state. The reaction is triggered by thermal structural fluctuations, and ultrafast changes in atomic partial charges play a key role. This is an example where a reaction in a small cluster can provide a model for a corresponding bulk process. The results support the proposed mechanism for production of HONO by hydrolysis of NO2 (N2O4). (ii) The reactions of gaseous HCl with N2O4 and N2O5 on liquid water surfaces. Ionization of HCl at the water/air interface is followed by nucleophilic attack of Cl(-) on N2O4 or N2O5. Both reactions proceed by an SN2 mechanism. The products are ClNO and ClNO2, precursors of atmospheric atomic chlorine. Because this mechanism cannot result from a cluster too small for HCl ionization, an extended water film model was simulated. The results explain ClNO formation experiments. Predicted ClNO2 formation is less efficient. (iii) Ionization of acids at ice surfaces. No ionization is found on ideal crystalline surfaces, but the process is efficient on

Comparative analysis of clustering methods for gene expression time course data

Directory of Open Access Journals (Sweden)

Ivan G. Costa

2004-01-01

Full Text Available This work performs a data driven comparative study of clustering methods used in the analysis of gene expression time courses (or time series. Five clustering methods found in the literature of gene expression analysis are compared: agglomerative hierarchical clustering, CLICK, dynamical clustering, k-means and self-organizing maps. In order to evaluate the methods, a k-fold cross-validation procedure adapted to unsupervised methods is applied. The accuracy of the results is assessed by the comparison of the partitions obtained in these experiments with gene annotation, such as protein function and series classification.

bcl::Cluster : A method for clustering biological molecules coupled with visualization in the Pymol Molecular Graphics System.

Science.gov (United States)

Alexander, Nathan; Woetzel, Nils; Meiler, Jens

2011-02-01

Clustering algorithms are used as data analysis tools in a wide variety of applications in Biology. Clustering has become especially important in protein structure prediction and virtual high throughput screening methods. In protein structure prediction, clustering is used to structure the conformational space of thousands of protein models. In virtual high throughput screening, databases with millions of drug-like molecules are organized by structural similarity, e.g. common scaffolds. The tree-like dendrogram structure obtained from hierarchical clustering can provide a qualitative overview of the results, which is important for focusing detailed analysis. However, in practice it is difficult to relate specific components of the dendrogram directly back to the objects of which it is comprised and to display all desired information within the two dimensions of the dendrogram. The current work presents a hierarchical agglomerative clustering method termed bcl::Cluster. bcl::Cluster utilizes the Pymol Molecular Graphics System to graphically depict dendrograms in three dimensions. This allows simultaneous display of relevant biological molecules as well as additional information about the clusters and the members comprising them.

Chemical exposure and leukemia clusters

International Nuclear Information System (INIS)

Cartwright, R.A.

1992-01-01

This paper draws attention to the heterogeneous distribution of leukemia in childhood and in adults. The topic of cluster reports and generalized clustering is addressed. These issues are applied to what is known of the risk factor for both adult and childhood leukemia. Finally, the significance of parental occupational exposure and childhood leukemia is covered. (author). 23 refs

Fast optimization of binary clusters using a novel dynamic lattice searching method

International Nuclear Information System (INIS)

Wu, Xia; Cheng, Wen

2014-01-01

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd) 79 clusters with DFT-fit parameters of Gupta potential

Cluster headache

Science.gov (United States)

Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

Structure and bonding in clusters

International Nuclear Information System (INIS)

Kumar, V.

1991-10-01

We review here the recent progress made in the understanding of the electronic and atomic structure of small clusters of s-p bonded materials using the density functional molecular dynamics technique within the local density approximation. Starting with a brief description of the method, results are presented for alkali metal clusters, clusters of divalent metals such as Mg and Be which show a transition from van der Waals or weak chemical bonding to metallic behaviour as the cluster size grows and clusters of Al, Sn and Sb. In the case of semiconductors, we discuss results for Si, Ge and GaAs clusters. Clusters of other materials such as P, C, S, and Se are also briefly discussed. From these and other available results we suggest the possibility of unique structures for the magic clusters. (author). 69 refs, 7 figs, 1 tab

Short-Term Wind Power Forecasting Based on Clustering Pre-Calculated CFD Method

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Yimei Wang

2018-04-01

Full Text Available To meet the increasing wind power forecasting (WPF demands of newly built wind farms without historical data, physical WPF methods are widely used. The computational fluid dynamics (CFD pre-calculated flow fields (CPFF-based WPF is a promising physical approach, which can balance well the competing demands of computational efficiency and accuracy. To enhance its adaptability for wind farms in complex terrain, a WPF method combining wind turbine clustering with CPFF is first proposed where the wind turbines in the wind farm are clustered and a forecasting is undertaken for each cluster. K-means, hierarchical agglomerative and spectral analysis methods are used to establish the wind turbine clustering models. The Silhouette Coefficient, Calinski-Harabaz index and within-between index are proposed as criteria to evaluate the effectiveness of the established clustering models. Based on different clustering methods and schemes, various clustering databases are built for clustering pre-calculated CFD (CPCC-based short-term WPF. For the wind farm case studied, clustering evaluation criteria show that hierarchical agglomerative clustering has reasonable results, spectral clustering is better and K-means gives the best performance. The WPF results produced by different clustering databases also prove the effectiveness of the three evaluation criteria in turn. The newly developed CPCC model has a much higher WPF accuracy than the CPFF model without using clustering techniques, both on temporal and spatial scales. The research provides supports for both the development and improvement of short-term physical WPF systems.

The Open Cluster Chemical Abundances and Mapping (OCCAM) Survey: Detailed Age and Abundance Gradients using DR12

Science.gov (United States)

Frinchaboy, Peter M.; Thompson, Benjamin A.; O'Connell, Julia; Meyer, Brianne; Donor, John; Majewski, Steven R.; Holtzman, Jon A.; Zasowski, Gail; Beers, Timothy C.; Beaton, Rachael; Cunha, Katia M. L.; Hearty, Fred; Nidever, David L.; Schiavon, Ricardo P.; Smith, Verne V.; Hayden, Michael R.

2015-01-01

We present detailed abundance results for Galactic open clusters as part of the Open Cluster Chemical Abundances and Mapping (OCCAM) Survey, which is based primarily on data from the Sloan Digital Sky Survey/ Apache Point Observatory Galactic Evolution Experiment. Using 100 open clusters from the uniformly observed complete SDSS-III/APOGEE-1 DR12 dataset, we present age and multi-element abundance gradients for the disk of the Milky Way.This work is supported by an NSF AAG grant AST-1311835.

A two-stage method for microcalcification cluster segmentation in mammography by deformable models

International Nuclear Information System (INIS)

Arikidis, N.; Kazantzi, A.; Skiadopoulos, S.; Karahaliou, A.; Costaridou, L.; Vassiou, K.

2015-01-01

Purpose: Segmentation of microcalcification (MC) clusters in x-ray mammography is a difficult task for radiologists. Accurate segmentation is prerequisite for quantitative image analysis of MC clusters and subsequent feature extraction and classification in computer-aided diagnosis schemes. Methods: In this study, a two-stage semiautomated segmentation method of MC clusters is investigated. The first stage is targeted to accurate and time efficient segmentation of the majority of the particles of a MC cluster, by means of a level set method. The second stage is targeted to shape refinement of selected individual MCs, by means of an active contour model. Both methods are applied in the framework of a rich scale-space representation, provided by the wavelet transform at integer scales. Segmentation reliability of the proposed method in terms of inter and intraobserver agreements was evaluated in a case sample of 80 MC clusters originating from the digital database for screening mammography, corresponding to 4 morphology types (punctate: 22, fine linear branching: 16, pleomorphic: 18, and amorphous: 24) of MC clusters, assessing radiologists’ segmentations quantitatively by two distance metrics (Hausdorff distance—HDIST cluster , average of minimum distance—AMINDIST cluster ) and the area overlap measure (AOM cluster ). The effect of the proposed segmentation method on MC cluster characterization accuracy was evaluated in a case sample of 162 pleomorphic MC clusters (72 malignant and 90 benign). Ten MC cluster features, targeted to capture morphologic properties of individual MCs in a cluster (area, major length, perimeter, compactness, and spread), were extracted and a correlation-based feature selection method yielded a feature subset to feed in a support vector machine classifier. Classification performance of the MC cluster features was estimated by means of the area under receiver operating characteristic curve (Az ± Standard Error) utilizing tenfold cross

Rich Ground State Chemical Ordering in Nanoparticles: Exact Solution of a Model for Ag-Au Clusters

DEFF Research Database (Denmark)

Larsen, Peter Mahler; Jacobsen, Karsten Wedel; Schiøtz, Jakob

2018-01-01

We show that nanoparticles can have very rich ground state chemical order. This is illustrated by determining the chemical ordering of Ag-Au 309-atom Mackay icosahedral nanoparticles. The energy of the nanoparticles is described using a cluster expansion model, and a Mixed Integer Programming (MIP......) approach is used to find the exact ground state configurations for all stoichiometries. The chemical ordering varies widely between the different stoichiometries, and display a rich zoo of structures with non-trivial ordering....

A comparison of heuristic and model-based clustering methods for dietary pattern analysis.

Science.gov (United States)

Greve, Benjamin; Pigeot, Iris; Huybrechts, Inge; Pala, Valeria; Börnhorst, Claudia

2016-02-01

Cluster analysis is widely applied to identify dietary patterns. A new method based on Gaussian mixture models (GMM) seems to be more flexible compared with the commonly applied k-means and Ward's method. In the present paper, these clustering approaches are compared to find the most appropriate one for clustering dietary data. The clustering methods were applied to simulated data sets with different cluster structures to compare their performance knowing the true cluster membership of observations. Furthermore, the three methods were applied to FFQ data assessed in 1791 children participating in the IDEFICS (Identification and Prevention of Dietary- and Lifestyle-Induced Health Effects in Children and Infants) Study to explore their performance in practice. The GMM outperformed the other methods in the simulation study in 72 % up to 100 % of cases, depending on the simulated cluster structure. Comparing the computationally less complex k-means and Ward's methods, the performance of k-means was better in 64-100 % of cases. Applied to real data, all methods identified three similar dietary patterns which may be roughly characterized as a 'non-processed' cluster with a high consumption of fruits, vegetables and wholemeal bread, a 'balanced' cluster with only slight preferences of single foods and a 'junk food' cluster. The simulation study suggests that clustering via GMM should be preferred due to its higher flexibility regarding cluster volume, shape and orientation. The k-means seems to be a good alternative, being easier to use while giving similar results when applied to real data.

Electronic and chemical properties of indium clusters

International Nuclear Information System (INIS)

Rayane, D.; Khardi, S.; Tribollet, B.; Broyer, M.; Melinon, P.; Cabaud, B.; Hoareau, A.

1989-01-01

Indium clusters are produced by the inert gas condensation technique. The ionization potentials are found higher for small clusters than for the Indium atom. This is explained by the p character of the bonding as in aluminium. Doubly charge clusters are also observed and fragmentation processes discussed. Finally small Indium clusters 3< n<9 are found very reactive with hydrocarbon. (orig.)

Advanced cluster methods for correlated-electron systems

Energy Technology Data Exchange (ETDEWEB)

Fischer, Andre

2015-04-27

In this thesis, quantum cluster methods are used to calculate electronic properties of correlated-electron systems. A special focus lies in the determination of the ground state properties of a 3/4 filled triangular lattice within the one-band Hubbard model. At this filling, the electronic density of states exhibits a so-called van Hove singularity and the Fermi surface becomes perfectly nested, causing an instability towards a variety of spin-density-wave (SDW) and superconducting states. While chiral d+id-wave superconductivity has been proposed as the ground state in the weak coupling limit, the situation towards strong interactions is unclear. Additionally, quantum cluster methods are used here to investigate the interplay of Coulomb interactions and symmetry-breaking mechanisms within the nematic phase of iron-pnictide superconductors. The transition from a tetragonal to an orthorhombic phase is accompanied by a significant change in electronic properties, while long-range magnetic order is not established yet. The driving force of this transition may not only be phonons but also magnetic or orbital fluctuations. The signatures of these scenarios are studied with quantum cluster methods to identify the most important effects. Here, cluster perturbation theory (CPT) and its variational extention, the variational cluster approach (VCA) are used to treat the respective systems on a level beyond mean-field theory. Short-range correlations are incorporated numerically exactly by exact diagonalization (ED). In the VCA, long-range interactions are included by variational optimization of a fictitious symmetry-breaking field based on a self-energy functional approach. Due to limitations of ED, cluster sizes are limited to a small number of degrees of freedom. For the 3/4 filled triangular lattice, the VCA is performed for different cluster symmetries. A strong symmetry dependence and finite-size effects make a comparison of the results from different clusters difficult

AutoSOME: a clustering method for identifying gene expression modules without prior knowledge of cluster number

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Cooper James B

2010-03-01

Full Text Available Abstract Background Clustering the information content of large high-dimensional gene expression datasets has widespread application in "omics" biology. Unfortunately, the underlying structure of these natural datasets is often fuzzy, and the computational identification of data clusters generally requires knowledge about cluster number and geometry. Results We integrated strategies from machine learning, cartography, and graph theory into a new informatics method for automatically clustering self-organizing map ensembles of high-dimensional data. Our new method, called AutoSOME, readily identifies discrete and fuzzy data clusters without prior knowledge of cluster number or structure in diverse datasets including whole genome microarray data. Visualization of AutoSOME output using network diagrams and differential heat maps reveals unexpected variation among well-characterized cancer cell lines. Co-expression analysis of data from human embryonic and induced pluripotent stem cells using AutoSOME identifies >3400 up-regulated genes associated with pluripotency, and indicates that a recently identified protein-protein interaction network characterizing pluripotency was underestimated by a factor of four. Conclusions By effectively extracting important information from high-dimensional microarray data without prior knowledge or the need for data filtration, AutoSOME can yield systems-level insights from whole genome microarray expression studies. Due to its generality, this new method should also have practical utility for a variety of data-intensive applications, including the results of deep sequencing experiments. AutoSOME is available for download at http://jimcooperlab.mcdb.ucsb.edu/autosome.

Radiation-Induced Chemical Dynamics in Ar Clusters Exposed to Strong X-Ray Pulses

Science.gov (United States)

Kumagai, Yoshiaki; Jurek, Zoltan; Xu, Weiqing; Fukuzawa, Hironobu; Motomura, Koji; Iablonskyi, Denys; Nagaya, Kiyonobu; Wada, Shin-ichi; Mondal, Subhendu; Tachibana, Tetsuya; Ito, Yuta; Sakai, Tsukasa; Matsunami, Kenji; Nishiyama, Toshiyuki; Umemoto, Takayuki; Nicolas, Christophe; Miron, Catalin; Togashi, Tadashi; Ogawa, Kanade; Owada, Shigeki; Tono, Kensuke; Yabashi, Makina; Son, Sang-Kil; Ziaja, Beata; Santra, Robin; Ueda, Kiyoshi

2018-06-01

We show that electron and ion spectroscopy reveals the details of the oligomer formation in Ar clusters exposed to an x-ray free electron laser (XFEL) pulse, i.e., chemical dynamics triggered by x rays. With guidance from a dedicated molecular dynamics simulation tool, we find that van der Waals bonding, the oligomer formation mechanism, and charge transfer among the cluster constituents significantly affect ionization dynamics induced by an XFEL pulse of moderate fluence. Our results clearly demonstrate that XFEL pulses can be used not only to "damage and destroy" molecular assemblies but also to modify and transform their molecular structure. The accuracy of the predictions obtained makes it possible to apply the cluster spectroscopy, in connection with the respective simulations, for estimation of the XFEL pulse fluence in the fluence regime below single-atom multiple-photon absorption, which is hardly accessible with other diagnostic tools.

A model-based clustering method to detect infectious disease transmission outbreaks from sequence variation.

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Rosemary M McCloskey

2017-11-01

Full Text Available Clustering infections by genetic similarity is a popular technique for identifying potential outbreaks of infectious disease, in part because sequences are now routinely collected for clinical management of many infections. A diverse number of nonparametric clustering methods have been developed for this purpose. These methods are generally intuitive, rapid to compute, and readily scale with large data sets. However, we have found that nonparametric clustering methods can be biased towards identifying clusters of diagnosis-where individuals are sampled sooner post-infection-rather than the clusters of rapid transmission that are meant to be potential foci for public health efforts. We develop a fundamentally new approach to genetic clustering based on fitting a Markov-modulated Poisson process (MMPP, which represents the evolution of transmission rates along the tree relating different infections. We evaluated this model-based method alongside five nonparametric clustering methods using both simulated and actual HIV sequence data sets. For simulated clusters of rapid transmission, the MMPP clustering method obtained higher mean sensitivity (85% and specificity (91% than the nonparametric methods. When we applied these clustering methods to published sequences from a study of HIV-1 genetic clusters in Seattle, USA, we found that the MMPP method categorized about half (46% as many individuals to clusters compared to the other methods. Furthermore, the mean internal branch lengths that approximate transmission rates were significantly shorter in clusters extracted using MMPP, but not by other methods. We determined that the computing time for the MMPP method scaled linearly with the size of trees, requiring about 30 seconds for a tree of 1,000 tips and about 20 minutes for 50,000 tips on a single computer. This new approach to genetic clustering has significant implications for the application of pathogen sequence analysis to public health, where

Using Star Clusters as Tracers of Star Formation and Chemical Evolution: The Chemical Enrichment History of the Large Magellanic Cloud

Science.gov (United States)

Chilingarian, Igor V.; Asa’d, Randa

2018-05-01

The star formation (SFH) and chemical enrichment (CEH) histories of Local Group galaxies are traditionally studied by analyzing their resolved stellar populations in a form of color–magnitude diagrams obtained with the Hubble Space Telescope. Star clusters can be studied in integrated light using ground-based telescopes to much larger distances. They represent snapshots of the chemical evolution of their host galaxy at different ages. Here we present a simple theoretical framework for the chemical evolution based on the instantaneous recycling approximation (IRA) model. We infer a CEH from an SFH and vice versa using observational data. We also present a more advanced model for the evolution of individual chemical elements that takes into account the contribution of supernovae type Ia. We demonstrate that ages, iron, and α-element abundances of 15 star clusters derived from the fitting of their integrated optical spectra reliably trace the CEH of the Large Magellanic Cloud obtained from resolved stellar populations in the age range 40 Myr age–metallicity relation. Moreover, the present-day total gas mass of the LMC estimated by the IRA model (6.2× {10}8 {M}ȯ ) matches within uncertainties the observed H I mass corrected for the presence of molecular gas (5.8+/- 0.5× {10}8 {M}ȯ ). We briefly discuss how our approach can be used to study SFHs of galaxies as distant as 10 Mpc at the level of detail that is currently available only in a handful of nearby Milky Way satellites. .

No Evidence of Chemical Abundance Variations in the Intermediate-age Cluster NGC 1783

Science.gov (United States)

Zhang, Hao; de Grijs, Richard; Li, Chengyuan; Wu, Xiaohan

2018-02-01

We have analyzed multi-passband photometric observations, obtained with the Hubble Space Telescope, of the massive (1.8 × 105 M ⊙), intermediate-age (1.8 Gyr-old) Large Magellanic Cloud star cluster NGC 1783. The morphology of the cluster’s red giant branch does not exhibit a clear broadening beyond its intrinsic width; the observed width is consistent with that owing to photometric uncertainties alone and independent of the photometric selection boundaries we applied to obtain our sample of red giant stars. The color dispersion of the cluster’s red giant stars around the best-fitting ridgeline is 0.062 ± 0.009 mag, which is equivalent to the width of 0.080 ± 0.001 mag derived from artificial simple stellar population tests, that is, tests based on single-age, single-metallicity stellar populations. NGC 1783 is comparably as massive as other star clusters that show clear evidence of multiple stellar populations. After incorporating mass-loss recipes from its current age of 1.8 Gyr to an age of 6 Gyr, NGC 1783 is expected to remain as massive as some other clusters that host clear multiple populations at these intermediate ages. If we were to assume that mass is an important driver of multiple population formation, then NGC 1783 should have exhibited clear evidence of chemical abundance variations. However, our results support the absence of any chemical abundance variations in NGC 1783.

International Trade of Croatian Chemical Industry Summary

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Goran Buturac

2009-07-01

Full Text Available In this paper Croatian chemical industry in international trade is analyzed by applying k-means cluster method. The work is oriented toward the role and contribution of individual product groups in total trade patterns of chemical industry. The RCA indicator, GL index, RUV indicator and the share of individual chemical products in the total export of chemical industry are used as variables. The products at the fourdigit level of the SITC are used as objects. The cluster of chemical products in which Croatia has comparative advantages contributes significantly in export structure. At the same time this cluster consists of a few product types thus indicating strong export concentration of Croatian chemical industry. Regarding of the value of RUV indicator, Croatian chemical industry benefits most in the international trade with antibiotics and medicines that contain antibiotics. Beside fertilizers, these two products have the greatest share in the export structure. The great majority of the chemical products have the low level of intra-industry trade specialization.

Boron-doped hydrogenated Al{sub 3} clusters: A material for hydrogen storage

Energy Technology Data Exchange (ETDEWEB)

Muz, İskender, E-mail: iskender.muz@nevsehir.edu.tr [Faculty of Education, Department of Science Education, Nevsehir Haci Bektas Veli University, 50300, Nevsehir (Turkey); Atiş, Murat [Kayseri Vocational School, Electricity and Energy Department, Erciyes University, 38300, Kayseri (Turkey)

2016-05-15

The energetic and structural stabilities of Al{sub 3}BH{sub 2n} (n = 0–6) clusters are investigated using ab initio calculations. Structural isomers are found using the stochastic search method to search for minima structures, followed by B3LYP optimizations; single-point CCSD(T) calculations are performed to compute relative energies. Chemical bonding analysis is also performed using the adaptive natural density partitioning method to investigate the chemical bonding in the clusters and to elucidate their structural evolution. Our results and analyses indicate that the stability of the boron-doped hydrogenated Al{sub 3} clusters increases as more hydrogen molecules are adsorbed, whereas the H{sub 2} loss energy decreases. The results are in good agreement with available theoretical findings. - Highlights: • The boron-doped hydrogenated Al{sub 3} clusters are generated using stochastic search method. • The energetic and structural stabilities are investigated in detail. • The chemical bonding analysis is performed by using AdNDP analysis. • The doping by boron allows development of better aluminum-based metal hydrides.

THE DETAILED CHEMICAL PROPERTIES OF M31 STAR CLUSTERS. I. Fe, ALPHA AND LIGHT ELEMENTS

Energy Technology Data Exchange (ETDEWEB)

Colucci, Janet E.; Bernstein, Rebecca A. [The Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101 (United States); Cohen, Judith G., E-mail: jcolucci@obs.carnegiescience.edu [Palomar Observatory, Mail Stop 105-24, California Institute of Technology, Pasadena, CA 91125 (United States)

2014-12-20

We present ages, [Fe/H] and abundances of the α elements Ca I, Si I, Ti I, Ti II, and light elements Mg I, Na I, and Al I for 31 globular clusters (GCs) in M31, which were obtained from high-resolution, high signal-to-noise ratio >60 echelle spectra of their integrated light (IL). All abundances and ages are obtained using our original technique for high-resolution IL abundance analysis of GCs. This sample provides a never before seen picture of the chemical history of M31. The GCs are dispersed throughout the inner and outer halo, from 2.5 kpc < R {sub M31} < 117 kpc. We find a range of [Fe/H] within 20 kpc of the center of M31, and a constant [Fe/H] ∼ – 1.6 for the outer halo clusters. We find evidence for at least one massive GC in M31 with an age between 1 and 5 Gyr. The α-element ratios are generally similar to the Milky Way GC and field star ratios. We also find chemical evidence for a late-time accretion origin for at least one cluster, which has a different abundance pattern than other clusters at similar metallicity. We find evidence for star-to-star abundance variations in Mg, Na, and Al in the GCs in our sample, and find correlations of Ca, Mg, Na, and possibly Al abundance ratios with cluster luminosity and velocity dispersion, which can potentially be used to constrain GC self-enrichment scenarios. Data presented here were obtained with the HIRES echelle spectrograph on the Keck I telescope.

Application of Different Extraction Methods for Investigation of Nonmetallic Inclusions and Clusters in Steels and Alloys

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Diana Janis

2014-01-01

Full Text Available The characterization of nonmetallic inclusions is of importance for the production of clean steel in order to improve the mechanical properties. In this respect, a three-dimensional (3D investigation is considered to be useful for an accurate evaluation of size, number, morphology of inclusions, and elementary distribution in each inclusion particle. In this study, the application of various extraction methods (chemical extraction/etching by acid or halogen-alcohol solutions, electrolysis, sputtering with glow discharge, and so on for 3D estimation of nonmetallic Al2O3 inclusions and clusters in high-alloyed steels was examined and discussed using an Fe-10 mass% Ni alloy and an 18/8 stainless steel deoxidized with Al. Advantages and limitations of different extraction methods for 3D investigations of inclusions and clusters were discussed in comparison to conventional two-dimensional (2D observations on a polished cross section of metal samples.

Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering

Directory of Open Access Journals (Sweden)

Susan Worner

2013-09-01

Full Text Available For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the co-occurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM, a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k

Quantum chemical study of the structure, spectroscopy and reactivity of NO+.(H2O)n=1-5 clusters

Science.gov (United States)

Linton, Kirsty A.; Wright, Timothy G.; Besley, Nicholas A.

2018-03-01

Quantum chemical methods including Møller-Plesset perturbation (MP2) theory and density functional theory (DFT) have been used to study the structure, spectroscopy and reactivity of NO+.(H2O)n=1-5 clusters. MP2/6-311++G** calculations are shown to describe the structure and spectroscopy of the clusters well. DFT calculations with exchange-correlation functionals with a low fraction of Hartree-Fock exchange give a binding energy of NO+.(H2O) that is too high and incorrectly predict the lowest energy structure of NO+.(H2O)2, and this error may be associated with a delocalization of charge onto the water molecule directly binding to NO+. Ab initio molecular dynamics (AIMD) simulations were performed to study the NO+.(H2O)5 H+.(H2O)4 + HONO reaction to investigate the formation of HONO from NO+.(H2O)5. Whether an intracluster reaction to form HONO is observed depends on the level of electronic structure theory used. Of note is that methods that accurately describe the relative energies of the product and reactant clusters did not show reactions on the timescales studied. This suggests that in the upper atmosphere the reaction may occur owing to the energy present in the NO+.(H2O)5 complex following its formation. This article is part of the theme issue `Modern theoretical chemistry'.

A NEW METHOD TO QUANTIFY X-RAY SUBSTRUCTURES IN CLUSTERS OF GALAXIES

Energy Technology Data Exchange (ETDEWEB)

Andrade-Santos, Felipe; Lima Neto, Gastao B.; Lagana, Tatiana F. [Departamento de Astronomia, Instituto de Astronomia, Geofisica e Ciencias Atmosfericas, Universidade de Sao Paulo, Geofisica e Ciencias Atmosfericas, Rua do Matao 1226, Cidade Universitaria, 05508-090 Sao Paulo, SP (Brazil)

2012-02-20

We present a new method to quantify substructures in clusters of galaxies, based on the analysis of the intensity of structures. This analysis is done in a residual image that is the result of the subtraction of a surface brightness model, obtained by fitting a two-dimensional analytical model ({beta}-model or Sersic profile) with elliptical symmetry, from the X-ray image. Our method is applied to 34 clusters observed by the Chandra Space Telescope that are in the redshift range z in [0.02, 0.2] and have a signal-to-noise ratio (S/N) greater than 100. We present the calibration of the method and the relations between the substructure level with physical quantities, such as the mass, X-ray luminosity, temperature, and cluster redshift. We use our method to separate the clusters in two sub-samples of high- and low-substructure levels. We conclude, using Monte Carlo simulations, that the method recuperates very well the true amount of substructure for small angular core radii clusters (with respect to the whole image size) and good S/N observations. We find no evidence of correlation between the substructure level and physical properties of the clusters such as gas temperature, X-ray luminosity, and redshift; however, analysis suggest a trend between the substructure level and cluster mass. The scaling relations for the two sub-samples (high- and low-substructure level clusters) are different (they present an offset, i.e., given a fixed mass or temperature, low-substructure clusters tend to be more X-ray luminous), which is an important result for cosmological tests using the mass-luminosity relation to obtain the cluster mass function, since they rely on the assumption that clusters do not present different scaling relations according to their dynamical state.

Sensitivity evaluation of dynamic speckle activity measurements using clustering methods

International Nuclear Information System (INIS)

Etchepareborda, Pablo; Federico, Alejandro; Kaufmann, Guillermo H.

2010-01-01

We evaluate and compare the use of competitive neural networks, self-organizing maps, the expectation-maximization algorithm, K-means, and fuzzy C-means techniques as partitional clustering methods, when the sensitivity of the activity measurement of dynamic speckle images needs to be improved. The temporal history of the acquired intensity generated by each pixel is analyzed in a wavelet decomposition framework, and it is shown that the mean energy of its corresponding wavelet coefficients provides a suited feature space for clustering purposes. The sensitivity obtained by using the evaluated clustering techniques is also compared with the well-known methods of Konishi-Fujii, weighted generalized differences, and wavelet entropy. The performance of the partitional clustering approach is evaluated using simulated dynamic speckle patterns and also experimental data.

An Extended Affinity Propagation Clustering Method Based on Different Data Density Types

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XiuLi Zhao

2015-01-01

Full Text Available Affinity propagation (AP algorithm, as a novel clustering method, does not require the users to specify the initial cluster centers in advance, which regards all data points as potential exemplars (cluster centers equally and groups the clusters totally by the similar degree among the data points. But in many cases there exist some different intensive areas within the same data set, which means that the data set does not distribute homogeneously. In such situation the AP algorithm cannot group the data points into ideal clusters. In this paper, we proposed an extended AP clustering algorithm to deal with such a problem. There are two steps in our method: firstly the data set is partitioned into several data density types according to the nearest distances of each data point; and then the AP clustering method is, respectively, used to group the data points into clusters in each data density type. Two experiments are carried out to evaluate the performance of our algorithm: one utilizes an artificial data set and the other uses a real seismic data set. The experiment results show that groups are obtained more accurately by our algorithm than OPTICS and AP clustering algorithm itself.

Swarm: robust and fast clustering method for amplicon-based studies

Science.gov (United States)

Rognes, Torbjørn; Quince, Christopher; de Vargas, Colomban; Dunthorn, Micah

2014-01-01

Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units. PMID:25276506

Swarm: robust and fast clustering method for amplicon-based studies

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Frédéric Mahé

2014-09-01

Full Text Available Popular de novo amplicon clustering methods suffer from two fundamental flaws: arbitrary global clustering thresholds, and input-order dependency induced by centroid selection. Swarm was developed to address these issues by first clustering nearly identical amplicons iteratively using a local threshold, and then by using clusters’ internal structure and amplicon abundances to refine its results. This fast, scalable, and input-order independent approach reduces the influence of clustering parameters and produces robust operational taxonomic units.

Chemical abundances in the globular clusters NGC6229 and NGC6779

Science.gov (United States)

Khamidullina, D. A.; Sharina, M. E.; Shimansky, V. V.; Davoust, E.

2014-10-01

Long-slit medium-resolution spectra of the Galactic globular clusters (GCs) NGC6229 and NGC6779, obtained with the CARELEC spectrograph at the 1.93-m telescope of the Haute-Provence observatory, have been used to determine the age, helium abundance (Y), and metallicity [Fe/H] as well as the first estimate of the abundances of C, N, O, Mg, Ca, Ti, and Cr for these objects. We solved this task by comparing the observed spectra and the integrated synthetic spectra, calculated with the use of the stellar atmosphere models with the parameters preset for the stars from these clusters. The model mass estimates, T eff, and log g were derived by comparing the observed "color-magnitude" diagrams and the theoretical isochrones. The summing-up of the synthetic blanketed stellar spectra was conducted according to the Chabrier mass function. To test the accuracy of the results, we estimated the chemical abundances, [Fe/H], log t, and Y for the NGC5904 and NGC6254 clusters, which, according to the literature, are considered to be the closest analogues of the two GCs of our study. Using the medium-resolution spectra from the library of Schiavon et al., we obtained for these two clusters a satisfactory agreement with the reported estimates for all the parameters within the errors. We derived the following cluster parameters. NGC6229: [Fe/H] = -1.65 dex, t = 12.6 Gyr, Y = 0.26, [ α/Fe] = 0.28 dex; NGC6779: [Fe/H] = -1.9 dex, t = 12.6 Gyr, Y = 0.23, [ α/Fe] = 0.08 dex; NGC5904: [Fe/H] = -1.6 dex, t = 12.6 Gyr, Y = 0.30, [ α/Fe] = 0.35 dex; NGC6254: [Fe/H] = -1.52 dex, t = 11.2 Gyr, Y = 0.30, [ α/Fe] = 0.025 dex. The value [ α/Fe] denotes the average of the Ca and Mg abundances.

An incremental DPMM-based method for trajectory clustering, modeling, and retrieval.

Science.gov (United States)

Hu, Weiming; Li, Xi; Tian, Guodong; Maybank, Stephen; Zhang, Zhongfei

2013-05-01

Trajectory analysis is the basis for many applications, such as indexing of motion events in videos, activity recognition, and surveillance. In this paper, the Dirichlet process mixture model (DPMM) is applied to trajectory clustering, modeling, and retrieval. We propose an incremental version of a DPMM-based clustering algorithm and apply it to cluster trajectories. An appropriate number of trajectory clusters is determined automatically. When trajectories belonging to new clusters arrive, the new clusters can be identified online and added to the model without any retraining using the previous data. A time-sensitive Dirichlet process mixture model (tDPMM) is applied to each trajectory cluster for learning the trajectory pattern which represents the time-series characteristics of the trajectories in the cluster. Then, a parameterized index is constructed for each cluster. A novel likelihood estimation algorithm for the tDPMM is proposed, and a trajectory-based video retrieval model is developed. The tDPMM-based probabilistic matching method and the DPMM-based model growing method are combined to make the retrieval model scalable and adaptable. Experimental comparisons with state-of-the-art algorithms demonstrate the effectiveness of our algorithm.

Relation between financial market structure and the real economy: comparison between clustering methods.

Science.gov (United States)

Musmeci, Nicoló; Aste, Tomaso; Di Matteo, T

2015-01-01

We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Relation between financial market structure and the real economy: comparison between clustering methods.

Directory of Open Access Journals (Sweden)

Nicoló Musmeci

Full Text Available We quantify the amount of information filtered by different hierarchical clustering methods on correlations between stock returns comparing the clustering structure with the underlying industrial activity classification. We apply, for the first time to financial data, a novel hierarchical clustering approach, the Directed Bubble Hierarchical Tree and we compare it with other methods including the Linkage and k-medoids. By taking the industrial sector classification of stocks as a benchmark partition, we evaluate how the different methods retrieve this classification. The results show that the Directed Bubble Hierarchical Tree can outperform other methods, being able to retrieve more information with fewer clusters. Moreover,we show that the economic information is hidden at different levels of the hierarchical structures depending on the clustering method. The dynamical analysis on a rolling window also reveals that the different methods show different degrees of sensitivity to events affecting financial markets, like crises. These results can be of interest for all the applications of clustering methods to portfolio optimization and risk hedging [corrected].

Symmetrized partial-wave method for density-functional cluster calculations

International Nuclear Information System (INIS)

Averill, F.W.; Painter, G.S.

1994-01-01

The computational advantage and accuracy of the Harris method is linked to the simplicity and adequacy of the reference-density model. In an earlier paper, we investigated one way the Harris functional could be extended to systems outside the limits of weakly interacting atoms by making the charge density of the interacting atoms self-consistent within the constraints of overlapping spherical atomic densities. In the present study, a method is presented for augmenting the interacting atom charge densities with symmetrized partial-wave expansions on each atomic site. The added variational freedom of the partial waves leads to a scheme capable of giving exact results within a given exchange-correlation approximation while maintaining many of the desirable convergence and stability properties of the original Harris method. Incorporation of the symmetry of the cluster in the partial-wave construction further reduces the level of computational effort. This partial-wave cluster method is illustrated by its application to the dimer C 2 , the hypothetical atomic cluster Fe 6 Al 8 , and the benzene molecule

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods.

Science.gov (United States)

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community.

Clustering Scientific Publications Based on Citation Relations: A Systematic Comparison of Different Methods

Science.gov (United States)

Šubelj, Lovro; van Eck, Nees Jan; Waltman, Ludo

2016-01-01

Clustering methods are applied regularly in the bibliometric literature to identify research areas or scientific fields. These methods are for instance used to group publications into clusters based on their relations in a citation network. In the network science literature, many clustering methods, often referred to as graph partitioning or community detection techniques, have been developed. Focusing on the problem of clustering the publications in a citation network, we present a systematic comparison of the performance of a large number of these clustering methods. Using a number of different citation networks, some of them relatively small and others very large, we extensively study the statistical properties of the results provided by different methods. In addition, we also carry out an expert-based assessment of the results produced by different methods. The expert-based assessment focuses on publications in the field of scientometrics. Our findings seem to indicate that there is a trade-off between different properties that may be considered desirable for a good clustering of publications. Overall, map equation methods appear to perform best in our analysis, suggesting that these methods deserve more attention from the bibliometric community. PMID:27124610

Charge-doping and chemical composition-driven magnetocrystalline anisotropy in CoPt core-shell alloy clusters

Science.gov (United States)

Ruiz-Díaz, P.; Muñoz-Navia, M.; Dorantes-Dávila, J.

2018-03-01

Charge-doping together with 3 d-4 d alloying emerges as promising mechanisms for tailoring the magnetic properties of low-dimensional systems. Here, throughout ab initio calculations, we present a systematic overview regarding the impact of both electron(hole) charge-doping and chemical composition on the magnetocrystalline anisotropy (MA) of CoPt core-shell alloy clusters. By taking medium-sized Co n Pt m ( N = n + m = 85) octahedral-like alloy nanoparticles for some illustrative core-sizes as examples, we found enhanced MA energies and large induced spin(orbital) moments in Pt-rich clusters. Moreover, depending on the Pt-core-size, both in-plane and off-plane directions of magnetization are observed. In general, the MA of these binary compounds further stabilizes upon charge-doping. In addition, in the clusters with small MA, the doping promotes magnetization switching. Insights into the microscopical origins of the MA behavior are associated to changes in the electronic structure of the clusters. [Figure not available: see fulltext.

A Latent Variable Clustering Method for Wireless Sensor Networks

DEFF Research Database (Denmark)

Vasilev, Vladislav; Iliev, Georgi; Poulkov, Vladimir

2016-01-01

In this paper we derive a clustering method based on the Hidden Conditional Random Field (HCRF) model in order to maximizes the performance of a wireless sensor. Our novel approach to clustering in this paper is in the application of an index invariant graph that we defined in a previous work and...

An external domino effects investment approach to improve cross-plant safety within chemical clusters

International Nuclear Information System (INIS)

Reniers, Genserik

2010-01-01

Every company situated within a chemical cluster faces the risk of being struck by an escalating accident at one of its neighbouring plants (the so-called external domino effect risks). These cross-plant risks can be reduced or eliminated if neighbouring companies are willing to invest in systems and measures to prevent them. However, since reducing such multi-plant risks does not lead to direct economic benefits, enterprises tend to be reluctant to invest more than needed for meeting minimal legal requirements and they tend to invest without collaborating. The suggested approach in this article indicates what information is required to evaluate the available investment options in external domino effects prevention. To this end, game theory is used as a promising scientific technique to investigate the decision-making process on investments in prevention measures simultaneously involving several plants. The game between two neighbouring chemical plants and their strategic investment behaviour regarding the prevention of external domino effects is described and an illustrative example is provided. Recommendations are formulated to advance cross-plant prevention investments in a two-company cluster.

Chemical Fingerprint and Quantitative Analysis for the Quality Evaluation of Platycladi cacumen by Ultra-performance Liquid Chromatography Coupled with Hierarchical Cluster Analysis.

Science.gov (United States)

Shan, Mingqiu; Li, Sam Fong Yau; Yu, Sheng; Qian, Yan; Guo, Shuchen; Zhang, Li; Ding, Anwei

2018-01-01

Platycladi cacumen (dried twigs and leaves of Platycladus orientalis (L.) Franco) is a frequently utilized Chinese medicinal herb. To evaluate the quality of the phytomedcine, an ultra-performance liquid chromatographic method with diode array detection was established for chemical fingerprinting and quantitative analysis. In this study, 27 batches of P. cacumen from different regions were collected for analysis. A chemical fingerprint with 20 common peaks was obtained using Similarity Evaluation System for Chromatographic Fingerprint of Traditional Chinese Medicine (Version 2004A). Among these 20 components, seven flavonoids (myricitrin, isoquercitrin, quercitrin, afzelin, cupressuflavone, amentoflavone and hinokiflavone) were identified and determined simultaneously. In the method validation, the seven analytes showed good regressions (R ≥ 0.9995) within linear ranges and good recoveries from 96.4% to 103.3%. Furthermore, with the contents of these seven flavonoids, hierarchical clustering analysis was applied to distinguish the 27 batches into five groups. The chemometric results showed that these groups were almost consistent with geographical positions and climatic conditions of the production regions. Integrating fingerprint analysis, simultaneous determination and hierarchical clustering analysis, the established method is rapid, sensitive, accurate and readily applicable, and also provides a significant foundation for quality control of P. cacumen efficiently. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

The Views of Turkish Pre-Service Teachers about Effectiveness of Cluster Method as a Teaching Writing Method

Science.gov (United States)

Kitis, Emine; Türkel, Ali

2017-01-01

The aim of this study is to find out Turkish pre-service teachers' views on effectiveness of cluster method as a writing teaching method. The Cluster Method can be defined as a connotative creative writing method. The way the method works is that the person who brainstorms on connotations of a word or a concept in abscence of any kind of…

Improvement of economic potential estimation methods for enterprise with potential branch clusters use

Directory of Open Access Journals (Sweden)

V.Ya. Nusinov

2017-08-01

Full Text Available The research determines that the current existing methods of enterprise’s economic potential estimation are based on the use of additive, multiplicative and rating models. It is determined that the existing methods have a row of defects. For example, not all the methods take into account the branch features of the analysis, and also the level of development of the enterprise comparatively with other enterprises. It is suggested to level such defects by an account at the estimation of potential integral level not only by branch features of enterprises activity but also by the intra-account economic clusterization of such enterprises. Scientific works which are connected with the using of clusters for the estimation of economic potential are generalized. According to the results of generalization it is determined that it is possible to distinguish 9 scientific approaches in this direction: the use of natural clusterization of enterprises with the purpose of estimation and increase of region potential; the use of natural clusterization of enterprises with the purpose of estimation and increase of industry potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of region potential; use of artificial clusterization of enterprises with the purpose of estimation and increase of industry potential; the use of artificial clusterization of enterprises with the purpose of clustering potential estimation; the use of artificial clusterization of enterprises with the purpose of estimation of clustering competitiveness potential; the use of natural (artificial clusterization for the estimation of clustering efficiency; the use of natural (artificial clusterization for the increase of level at region (industries development; the use of methods of economic potential of region (industries estimation or its constituents for the construction of the clusters. It is determined that the use of clusterization method in

Interplay between experiments and calculations for organometallic clusters and caged clusters

International Nuclear Information System (INIS)

Nakajima, Atsushi

2015-01-01

Clusters consisting of 10-1000 atoms exhibit size-dependent electronic and geometric properties. In particular, composite clusters consisting of several elements and/or components provide a promising way for a bottom-up approach for designing functional advanced materials, because the functionality of the composite clusters can be optimized not only by the cluster size but also by their compositions. In the formation of composite clusters, their geometric symmetry and dimensionality are emphasized to control the physical and chemical properties, because selective and anisotropic enhancements for optical, chemical, and magnetic properties can be expected. Organometallic clusters and caged clusters are demonstrated as a representative example of designing the functionality of the composite clusters. Organometallic vanadium-benzene forms a one dimensional sandwich structure showing ferromagnetic behaviors and anomalously large HOMO-LUMO gap differences of two spin orbitals, which can be regarded as spin-filter components for cluster-based spintronic devices. Caged clusters of aluminum (Al) are well stabilized both geometrically and electronically at Al 12 X, behaving as a “superatom”

Kernel method for clustering based on optimal target vector

International Nuclear Information System (INIS)

Angelini, Leonardo; Marinazzo, Daniele; Pellicoro, Mario; Stramaglia, Sebastiano

2006-01-01

We introduce Ising models, suitable for dichotomic clustering, with couplings that are (i) both ferro- and anti-ferromagnetic (ii) depending on the whole data-set and not only on pairs of samples. Couplings are determined exploiting the notion of optimal target vector, here introduced, a link between kernel supervised and unsupervised learning. The effectiveness of the method is shown in the case of the well-known iris data-set and in benchmarks of gene expression levels, where it works better than existing methods for dichotomic clustering

Developing cluster strategy of apples dodol SMEs by integration K-means clustering and analytical hierarchy process method

Science.gov (United States)

Mustaniroh, S. A.; Effendi, U.; Silalahi, R. L. R.; Sari, T.; Ala, M.

2018-03-01

The purposes of this research were to determine the grouping of apples dodol small and medium enterprises (SMEs) in Batu City and to determine an appropriate development strategy for each cluster. The methods used for clustering SMEs was k-means. The Analytical Hierarchy Process (AHP) approach was then applied to determine the development strategy priority for each cluster. The variables used in grouping include production capacity per month, length of operation, investment value, average sales revenue per month, amount of SMEs assets, and the number of workers. Several factors were considered in AHP include industry cluster, government, as well as related and supporting industries. Data was collected using the methods of questionaire and interviews. SMEs respondents were selected among SMEs appels dodol in Batu City using purposive sampling. The result showed that two clusters were formed from five apples dodol SMEs. The 1stcluster of apples dodol SMEs, classified as small enterprises, included SME A, SME C, and SME D. The 2ndcluster of SMEs apples dodol, classified as medium enterprises, consisted of SME B and SME E. The AHP results indicated that the priority development strategy for the 1stcluster of apples dodol SMEs was improving quality and the product standardisation, while for the 2nd cluster was increasing the marketing access.

Excess electrons in methanol clusters: Beyond the one-electron picture

Science.gov (United States)

Pohl, Gábor; Mones, Letif; Turi, László

2016-10-01

We performed a series of comparative quantum chemical calculations on various size negatively charged methanol clusters, ("separators=" CH 3 OH ) n - . The clusters are examined in their optimized geometries (n = 2-4), and in geometries taken from mixed quantum-classical molecular dynamics simulations at finite temperature (n = 2-128). These latter structures model potential electron binding sites in methanol clusters and in bulk methanol. In particular, we compute the vertical detachment energy (VDE) of an excess electron from increasing size methanol cluster anions using quantum chemical computations at various levels of theory including a one-electron pseudopotential model, several density functional theory (DFT) based methods, MP2 and coupled-cluster CCSD(T) calculations. The results suggest that at least four methanol molecules are needed to bind an excess electron on a hydrogen bonded methanol chain in a dipole bound state. Larger methanol clusters are able to form stronger interactions with an excess electron. The two simulated excess electron binding motifs in methanol clusters, interior and surface states, correlate well with distinct, experimentally found VDE tendencies with size. Interior states in a solvent cavity are stabilized significantly stronger than electron states on cluster surfaces. Although we find that all the examined quantum chemistry methods more or less overestimate the strength of the experimental excess electron stabilization, MP2, LC-BLYP, and BHandHLYP methods with diffuse basis sets provide a significantly better estimate of the VDE than traditional DFT methods (BLYP, B3LYP, X3LYP, PBE0). A comparison to the better performing many electron methods indicates that the examined one-electron pseudopotential can be reasonably used in simulations for systems of larger size.

Molecular cluster theory of chemical bonding in actinide oxide

International Nuclear Information System (INIS)

Ellis, D.E.; Gubanov, V.A.; Rosen, A.

1978-01-01

The electronic structure of actinide monoxides AcO and dioxides AcO 2 , where Ac = Th, U, Np, Pu, Am, Cm and Bk has been studied by molecular cluster methods based on the first-principles one-electron local density theory. Molecular orbitals for nearest neighbor clusters AcO 10- 6 and AcO 12- 8 representative of monoxide and dioxide lattices were obtained using non-relativistic spin-restricted and spin-polarized Hartree-Fock-Slater models for the entire series. Fully relativistic Dirac-Slater calculations were performed for ThO, UO and NpO in order to explore magnitude of spin-orbit splittings and level shifts in valence structure. Self-consistent iterations were carried out for NpO, in which the NpO 6 cluster was embedded in the molecular field of the solid. Finally, a ''moment polarized'' model which combines both spin-polarization and relativistic effects in a consistent fashion was applied to the NpO system. Covalent mixing of oxygen 2p and Ac 5f orbitals was found to increase rapidly across the actinide series; metal s,p,d covalency was found to be nearly constant. Mulliken atomic population analysis of cluster eigenvectors shows that free-ion crystal field models are unreliable, except for the light actinides. X-ray photoelectron line shapes have been calculated and are found to compare rather well with experimental data on the dioxides

Developing a Clustering-Based Empirical Bayes Analysis Method for Hotspot Identification

Directory of Open Access Journals (Sweden)

Yajie Zou

2017-01-01

Full Text Available Hotspot identification (HSID is a critical part of network-wide safety evaluations. Typical methods for ranking sites are often rooted in using the Empirical Bayes (EB method to estimate safety from both observed crash records and predicted crash frequency based on similar sites. The performance of the EB method is highly related to the selection of a reference group of sites (i.e., roadway segments or intersections similar to the target site from which safety performance functions (SPF used to predict crash frequency will be developed. As crash data often contain underlying heterogeneity that, in essence, can make them appear to be generated from distinct subpopulations, methods are needed to select similar sites in a principled manner. To overcome this possible heterogeneity problem, EB-based HSID methods that use common clustering methodologies (e.g., mixture models, K-means, and hierarchical clustering to select “similar” sites for building SPFs are developed. Performance of the clustering-based EB methods is then compared using real crash data. Here, HSID results, when computed on Texas undivided rural highway cash data, suggest that all three clustering-based EB analysis methods are preferred over the conventional statistical methods. Thus, properly classifying the road segments for heterogeneous crash data can further improve HSID accuracy.

Consensus of satellite cluster flight using an energy-matching optimal control method

Science.gov (United States)

Luo, Jianjun; Zhou, Liang; Zhang, Bo

2017-11-01

This paper presents an optimal control method for consensus of satellite cluster flight under a kind of energy matching condition. Firstly, the relation between energy matching and satellite periodically bounded relative motion is analyzed, and the satellite energy matching principle is applied to configure the initial conditions. Then, period-delayed errors are adopted as state variables to establish the period-delayed errors dynamics models of a single satellite and the cluster. Next a novel satellite cluster feedback control protocol with coupling gain is designed, so that the satellite cluster periodically bounded relative motion consensus problem (period-delayed errors state consensus problem) is transformed to the stability of a set of matrices with the same low dimension. Based on the consensus region theory in the research of multi-agent system consensus issues, the coupling gain can be obtained to satisfy the requirement of consensus region and decouple the satellite cluster information topology and the feedback control gain matrix, which can be determined by Linear quadratic regulator (LQR) optimal method. This method can realize the consensus of satellite cluster period-delayed errors, leading to the consistency of semi-major axes (SMA) and the energy-matching of satellite cluster. Then satellites can emerge the global coordinative cluster behavior. Finally the feasibility and effectiveness of the present energy-matching optimal consensus for satellite cluster flight is verified through numerical simulations.

Chemical Abundances of Red Giant Branch Stars in the Globular Cluster NGC 288

Science.gov (United States)

Hsyu, Tiffany; Johnson, C. I.; Pilachowski, C. A.; Lee, Y.; Rich, R. M.

2013-01-01

We present chemical abundances and radial velocities for ~30 red giant branch (RGB) stars in the globular cluster NGC 288. The results are based on moderate resolution (R≈18,000) and moderate signal-to-noise ratio 50-75) obtained with the Hydra multi-object spectrograph on the Blanco 4m telescope. NGC 288 has been shown to exhibit two separate RGBs and we investigate possible differences in metallicity and/or light element abundances between stars on each branch. We present a new filter tracing for the CTIO Calcium HK narrow band filter and explore its effects on previous globular cluster color-magnitude diagrams. We also compare the light element abundance patterns of NGC 288 to those of other similar metallicity halo clusters. This material is based upon work supported by the National Science Foundation under award No.AST-1003201 to C.I.J. C.A.P. gratefully acknowledges support from the Daniel Kirkwood Research Fund at Indiana University. R.M.R. acknowledges support from NSF grants AST-0709479 and AST-121120995.

Structural and optical properties of Si-doped Ag clusters

KAUST Repository

Mokkath, Junais Habeeb

2014-03-06

The structural and optical properties of AgN and Ag N-1Si1 (neutral, cationic, and anionic) clusters (N = 5 to 12) are systematically investigated using the density functional based tight binding method and time-dependent density functional theory, providing insight into recent experiments. The gap between the highest occupied and lowest unoccupied molecular orbitals and therefore the optical spectrum vary significantly under Si doping, which enables flexible tuning of the chemical and optical properties of Ag clusters. © 2014 American Chemical Society.

Structural and optical properties of Si-doped Ag clusters

KAUST Repository

Mokkath, Junais Habeeb; Schwingenschlö gl, Udo

2014-01-01

The structural and optical properties of AgN and Ag N-1Si1 (neutral, cationic, and anionic) clusters (N = 5 to 12) are systematically investigated using the density functional based tight binding method and time-dependent density functional theory, providing insight into recent experiments. The gap between the highest occupied and lowest unoccupied molecular orbitals and therefore the optical spectrum vary significantly under Si doping, which enables flexible tuning of the chemical and optical properties of Ag clusters. © 2014 American Chemical Society.

Quantum chemical study of the structure, spectroscopy and reactivity of NO+.(H2O) n=1-5 clusters.

Science.gov (United States)

Linton, Kirsty A; Wright, Timothy G; Besley, Nicholas A

2018-03-13

Quantum chemical methods including Møller-Plesset perturbation (MP2) theory and density functional theory (DFT) have been used to study the structure, spectroscopy and reactivity of NO + (H 2 O) n =1-5 clusters. MP2/6-311++G** calculations are shown to describe the structure and spectroscopy of the clusters well. DFT calculations with exchange-correlation functionals with a low fraction of Hartree-Fock exchange give a binding energy of NO + (H 2 O) that is too high and incorrectly predict the lowest energy structure of NO + (H 2 O) 2 , and this error may be associated with a delocalization of charge onto the water molecule directly binding to NO + Ab initio molecular dynamics (AIMD) simulations were performed to study the NO + (H 2 O) 5 [Formula: see text] H + (H 2 O) 4 + HONO reaction to investigate the formation of HONO from NO + (H 2 O) 5 Whether an intracluster reaction to form HONO is observed depends on the level of electronic structure theory used. Of note is that methods that accurately describe the relative energies of the product and reactant clusters did not show reactions on the timescales studied. This suggests that in the upper atmosphere the reaction may occur owing to the energy present in the NO + (H 2 O) 5 complex following its formation.This article is part of the theme issue 'Modern theoretical chemistry'. © 2018 The Author(s).

Kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams

International Nuclear Information System (INIS)

Makarov, Grigorii N

2011-01-01

The temperature (internal energy) of clusters and nanoparticles is an important physical parameter which affects many of their properties and the character of processes they are involved in. At the same time, determining the temperature of free clusters and nanoparticles in molecular beams is a rather complicated problem because the temperature of small particles depends on their size. In this paper, recently developed kinetic methods for measuring the temperature of clusters and nanoparticles in molecular beams are reviewed. The definition of temperature in the present context is given, and how the temperature affects the properties of and the processes involving the particles is discussed. The temperature behavior of clusters and nanoparticles near a phase transition point is analyzed. Early methods for measuring the temperature of large clusters are briefly described. It is shown that, compared to other methods, new kinetic methods are more universal and applicable for determining the temperature of clusters and nanoparticles of practically any size and composition. The future development and applications of these methods are outlined. (reviews of topical problems)

A semantics-based method for clustering of Chinese web search results

Science.gov (United States)

Zhang, Hui; Wang, Deqing; Wang, Li; Bi, Zhuming; Chen, Yong

2014-01-01

Information explosion is a critical challenge to the development of modern information systems. In particular, when the application of an information system is over the Internet, the amount of information over the web has been increasing exponentially and rapidly. Search engines, such as Google and Baidu, are essential tools for people to find the information from the Internet. Valuable information, however, is still likely submerged in the ocean of search results from those tools. By clustering the results into different groups based on subjects automatically, a search engine with the clustering feature allows users to select most relevant results quickly. In this paper, we propose an online semantics-based method to cluster Chinese web search results. First, we employ the generalised suffix tree to extract the longest common substrings (LCSs) from search snippets. Second, we use the HowNet to calculate the similarities of the words derived from the LCSs, and extract the most representative features by constructing the vocabulary chain. Third, we construct a vector of text features and calculate snippets' semantic similarities. Finally, we improve the Chameleon algorithm to cluster snippets. Extensive experimental results have shown that the proposed algorithm has outperformed over the suffix tree clustering method and other traditional clustering methods.

Infrared Multiple Photon Dissociation Spectroscopy Of Metal Cluster-Adducts

Science.gov (United States)

Cox, D. M.; Kaldor, A.; Zakin, M. R.

1987-01-01

Recent development of the laser vaporization technique combined with mass-selective detection has made possible new studies of the fundamental chemical and physical properties of unsupported transition metal clusters as a function of the number of constituent atoms. A variety of experimental techniques have been developed in our laboratory to measure ionization threshold energies, magnetic moments, and gas phase reactivity of clusters. However, studies have so far been unable to determine the cluster structure or the chemical state of chemisorbed species on gas phase clusters. The application of infrared multiple photon dissociation IRMPD to obtain the IR absorption properties of metal cluster-adsorbate species in a molecular beam is described here. Specifically using a high power, pulsed CO2 laser as the infrared source, the IRMPD spectrum for methanol chemisorbed on small iron clusters is measured as a function of the number of both iron atoms and methanols in the complex for different methanol isotopes. Both the feasibility and potential utility of IRMPD for characterizing metal cluster-adsorbate interactions are demonstrated. The method is generally applicable to any cluster or cluster-adsorbate system dependent only upon the availability of appropriate high power infrared sources.

Blocked inverted indices for exact clustering of large chemical spaces

NARCIS (Netherlands)

Thiel, P.; Sach-Peltason, L.; Ottmann, C.; Kohlbacher, O.

2014-01-01

The calculation of pairwise compound similarities based on fingerprints is one of the fundamental tasks in chemoinformatics. Methods for efficient calculation of compound similarities are of utmost importance for various applications like similarity searching or library clustering. With the

A novel clustering and supervising users' profiles method

Institute of Scientific and Technical Information of China (English)

Zhu Mingfu; Zhang Hongbin; Song Fangyun

2005-01-01

To better understand different users' accessing intentions, a novel clustering and supervising method based on accessing path is presented. This method divides users' interest space to express the distribution of users' interests, and directly to instruct the constructing process of web pages indexing for advanced performance.

Agent-based method for distributed clustering of textual information

Science.gov (United States)

Potok, Thomas E [Oak Ridge, TN; Reed, Joel W [Knoxville, TN; Elmore, Mark T [Oak Ridge, TN; Treadwell, Jim N [Louisville, TN

2010-09-28

A computer method and system for storing, retrieving and displaying information has a multiplexing agent (20) that calculates a new document vector (25) for a new document (21) to be added to the system and transmits the new document vector (25) to master cluster agents (22) and cluster agents (23) for evaluation. These agents (22, 23) perform the evaluation and return values upstream to the multiplexing agent (20) based on the similarity of the document to documents stored under their control. The multiplexing agent (20) then sends the document (21) and the document vector (25) to the master cluster agent (22), which then forwards it to a cluster agent (23) or creates a new cluster agent (23) to manage the document (21). The system also searches for stored documents according to a search query having at least one term and identifying the documents found in the search, and displays the documents in a clustering display (80) of similarity so as to indicate similarity of the documents to each other.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

Science.gov (United States)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

A PROBABILISTIC EMBEDDING CLUSTERING METHOD FOR URBAN STRUCTURE DETECTION

Directory of Open Access Journals (Sweden)

X. Lin

2017-09-01

Full Text Available Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM to find latent features from high dimensional urban sensing data by “learning” via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Communication: Time-dependent optimized coupled-cluster method for multielectron dynamics

Science.gov (United States)

Sato, Takeshi; Pathak, Himadri; Orimo, Yuki; Ishikawa, Kenichi L.

2018-02-01

Time-dependent coupled-cluster method with time-varying orbital functions, called time-dependent optimized coupled-cluster (TD-OCC) method, is formulated for multielectron dynamics in an intense laser field. We have successfully derived the equations of motion for CC amplitudes and orthonormal orbital functions based on the real action functional, and implemented the method including double excitations (TD-OCCD) and double and triple excitations (TD-OCCDT) within the optimized active orbitals. The present method is size extensive and gauge invariant, a polynomial cost-scaling alternative to the time-dependent multiconfiguration self-consistent-field method. The first application of the TD-OCC method of intense-laser driven correlated electron dynamics in Ar atom is reported.

Performance Analysis of Unsupervised Clustering Methods for Brain Tumor Segmentation

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Tushar H Jaware

2013-10-01

Full Text Available Medical image processing is the most challenging and emerging field of neuroscience. The ultimate goal of medical image analysis in brain MRI is to extract important clinical features that would improve methods of diagnosis & treatment of disease. This paper focuses on methods to detect & extract brain tumour from brain MR images. MATLAB is used to design, software tool for locating brain tumor, based on unsupervised clustering methods. K-Means clustering algorithm is implemented & tested on data base of 30 images. Performance evolution of unsupervised clusteringmethods is presented.

Radionuclide identification using subtractive clustering method

International Nuclear Information System (INIS)

Farias, Marcos Santana; Mourelle, Luiza de Macedo

2011-01-01

Radionuclide identification is crucial to planning protective measures in emergency situations. This paper presents the application of a method for a classification system of radioactive elements with a fast and efficient response. To achieve this goal is proposed the application of subtractive clustering algorithm. The proposed application can be implemented in reconfigurable hardware, a flexible medium to implement digital hardware circuits. (author)

Chemical Abundances of Red Giant Branch Stars in the Globular Clusters NGC 6333 and NGC 6366

Science.gov (United States)

Johnson, Christian I.; Rich, R. M.; Pilachowski, C. A.; Kunder, A. M.

2013-01-01

We present chemical abundances and radial velocities for >20 red giant branch (RGB) stars in the Galactic globular clusters NGC 6333 ([Fe/H]≈-1.8) and NGC 6366 ([Fe/H]≈-0.6). The results are based on moderate resolution (R=18,000), high signal-to-noise ratio (>100) spectra obtained with the Hydra multifiber positioner and bench spectrograph on the WIYN 3.5m telescope at Kitt Peak National Observatory. Both objects are likely associated with the Galactic bulge globular cluster system, and we therefore compare the cluster abundance patterns with those of nearby bulge field stars. Additionally, we investigate differences in the O-Na anticorrelation and neutron-capture element dispersion between the two clusters, and compare their abundance patterns with those of similar metallicity halo globular clusters. This material is based upon work supported by the National Science Foundation under award No. AST-1003201 to C.I.J. C.A.P. gratefully acknowledges support from the Daniel Kirkwood Research Fund at Indiana University. R.M.R. acknowledges support from NSF grant AST-0709479 and AST-121120995.

Experimental studies of the chemistry of metal clusters

International Nuclear Information System (INIS)

Parks, E.K.; Riley, S.J.

1988-01-01

The procedures for studying chemical reactions of metal clusters in a continuous-flow reactor are described, and examples of such studies are given. Experiments to be discussed include kinetics and thermodynamics measurements, and determination of the composition of clusters saturated with various adsorbate reagents. Specific systems to be covered include the reaction of iron clusters with ammonia and with hydrogen, the reaction of nickel clusters with hydrogen and with ammonia, and the reaction of platinum clusters with ethylene. The last two reactions are characterized by complex, multi-step processes that lead to adsorbate decomposition and hydrogen desorption from the clusters. Methods for probing these processes will be discussed. 26 refs., 8 figs

Recent advances in coupled-cluster methods

CERN Document Server

Bartlett, Rodney J

1997-01-01

Today, coupled-cluster (CC) theory has emerged as the most accurate, widely applicable approach for the correlation problem in molecules. Furthermore, the correct scaling of the energy and wavefunction with size (i.e. extensivity) recommends it for studies of polymers and crystals as well as molecules. CC methods have also paid dividends for nuclei, and for certain strongly correlated systems of interest in field theory.In order for CC methods to have achieved this distinction, it has been necessary to formulate new, theoretical approaches for the treatment of a variety of essential quantities

Multishell method: Exact treatment of a cluster in an effective medium

International Nuclear Information System (INIS)

Gonis, A.; Garland, J.W.

1977-01-01

A method is presented for the exact determination of the Green's function of a cluster embedded in a given effective medium. This method, the multishell method, is applicable even to systems with off-diagonal disorder, extended-range hopping, multiple bands, and/or hybridization, and is computationally practicable for any system described by a tight-binding or interpolation-scheme Hamiltonian. It allows one to examine the effects of local environment on the densities of states and site spectral weight functions of disordered systems. For any given analytic effective medium characterized by a non-negative density of states the method yields analytic cluster Green's functions and non-negative site spectral weight functions. Previous methods used for the calculation of the Green's function of a cluster embedded in a given effective medium have not been exact. The results of numerical calculations for model systems show that even the best of these previous methods can lead to substantial errors, at least for small clusters in two- and three-dimensional lattices. These results also show that fluctuations in local environment have large effects on site spectral weight functions, even in cases in which the single-site coherent-potential approximation yields an accurate overall density of states

Novel Clustering Method Based on K-Medoids and Mobility Metric

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Y. Hamzaoui

2018-06-01

Full Text Available The structure and constraint of MANETS influence negatively the performance of QoS, moreover the main routing protocols proposed generally operate in flat routing. Hence, this structure gives the bad results of QoS when the network becomes larger and denser. To solve this problem we use one of the most popular methods named clustering. The present paper comes within the frameworks of research to improve the QoS in MANETs. In this paper we propose a new algorithm of clustering based on the new mobility metric and K-Medoid to distribute the nodes into several clusters. Intuitively our algorithm can give good results in terms of stability of the cluster, and can also extend life time of cluster head.

Cluster dynamics transcending chemical dynamics toward nuclear fusion.

Science.gov (United States)

Heidenreich, Andreas; Jortner, Joshua; Last, Isidore

2006-07-11

Ultrafast cluster dynamics encompasses femtosecond nuclear dynamics, attosecond electron dynamics, and electron-nuclear dynamics in ultraintense laser fields (peak intensities 10(15)-10(20) W.cm(-2)). Extreme cluster multielectron ionization produces highly charged cluster ions, e.g., (C(4+)(D(+))(4))(n) and (D(+)I(22+))(n) at I(M) = 10(18) W.cm(-2), that undergo Coulomb explosion (CE) with the production of high-energy (5 keV to 1 MeV) ions, which can trigger nuclear reactions in an assembly of exploding clusters. The laser intensity and the cluster size dependence of the dynamics and energetics of CE of (D(2))(n), (HT)(n), (CD(4))(n), (DI)(n), (CD(3)I)(n), and (CH(3)I)(n) clusters were explored by electrostatic models and molecular dynamics simulations, quantifying energetic driving effects, and kinematic run-over effects. The optimization of table-top dd nuclear fusion driven by CE of deuterium containing heteroclusters is realized for light-heavy heteroclusters of the largest size, which allows for the prevalence of cluster vertical ionization at the highest intensity of the laser field. We demonstrate a 7-orders-of-magnitude enhancement of the yield of dd nuclear fusion driven by CE of light-heavy heteroclusters as compared with (D(2))(n) clusters of the same size. Prospective applications for the attainment of table-top nucleosynthesis reactions, e.g., (12)C(P,gamma)(13)N driven by CE of (CH(3)I)(n) clusters, were explored.

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

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Huanhuan Li

2017-08-01

Full Text Available The Shipboard Automatic Identification System (AIS is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW, a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis.

Science.gov (United States)

Li, Huanhuan; Liu, Jingxian; Liu, Ryan Wen; Xiong, Naixue; Wu, Kefeng; Kim, Tai-Hoon

2017-08-04

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with

An external domino effects investment approach to improve cross-plant safety within chemical clusters.

Science.gov (United States)

Reniers, Genserik

2010-05-15

Every company situated within a chemical cluster faces the risk of being struck by an escalating accident at one of its neighbouring plants (the so-called external domino effect risks). These cross-plant risks can be reduced or eliminated if neighbouring companies are willing to invest in systems and measures to prevent them. However, since reducing such multi-plant risks does not lead to direct economic benefits, enterprises tend to be reluctant to invest more than needed for meeting minimal legal requirements and they tend to invest without collaborating. The suggested approach in this article indicates what information is required to evaluate the available investment options in external domino effects prevention. To this end, game theory is used as a promising scientific technique to investigate the decision-making process on investments in prevention measures simultaneously involving several plants. The game between two neighbouring chemical plants and their strategic investment behaviour regarding the prevention of external domino effects is described and an illustrative example is provided. Recommendations are formulated to advance cross-plant prevention investments in a two-company cluster. Copyright (c) 2009 Elsevier B.V. All rights reserved.

SPATIALLY RESOLVED SPECTROSCOPY AND CHEMICAL HISTORY OF STAR-FORMING GALAXIES IN THE HERCULES CLUSTER: THE EFFECTS OF THE ENVIRONMENT

International Nuclear Information System (INIS)

Petropoulou, V.; Vilchez, J.; Iglesias-Paramo, J.; Cedres, B.; Papaderos, P.; Magrini, L.; Reverte, D.

2011-01-01

Spatially resolved spectroscopy has been obtained for a sample of 27 star-forming (SF) galaxies selected from our deep Hα survey of the Hercules cluster. We have applied spectral synthesis models to all emission-line spectra of this sample using the population synthesis code STARLIGHT and have obtained fundamental parameters of stellar components such as mean metallicity and age. The emission-line spectra were corrected for underlying stellar absorption using these spectral synthesis models. Line fluxes were measured and O/H and N/O gas chemical abundances were obtained using the latest empirical calibrations. We have derived the masses and total luminosities of the galaxies using available Sloan Digital Sky Survey broadband photometry. The effects of cluster environment on the chemical evolution of galaxies and on their mass-metallicity (MZ) and luminosity-metallicity (LZ) relations were studied by combining the derived gas metallicities, the mean stellar metallicities and ages, the masses and luminosities of the galaxies, and their existing H I data. Our Hercules SF galaxies are divided into three main subgroups: (1) chemically evolved spirals with truncated ionized-gas disks and nearly flat oxygen gradients, demonstrating the effect of ram-pressure stripping; (2) chemically evolved dwarfs/irregulars populating the highest local densities, possible products of tidal interactions in preprocessing events; and (3) less metallic dwarf galaxies that appear to be 'newcomers' to the cluster and are experiencing pressure-triggered star formation. Most Hercules SF galaxies follow well-defined MZ and LZ sequences (for both O/H and N/O), though the dwarf/irregular galaxies located at the densest regions appear to be outliers to these global relations, suggesting a physical reason for the dispersion in these fundamental relations. The Hercules cluster appears to be currently assembling via the merger of smaller substructures, providing an ideal laboratory where the local

Application of the Fenske-Hall molecular orbital method to the calculation of 11B NMR chemical shifts. Antipodal substituent effects in deltahedral clusters

International Nuclear Information System (INIS)

Fehlner, T.P.; Czech, P.T.; Fenske, R.F.

1990-01-01

Utilizing Fenske-Hall wave functions and eigenvalues combined with the Ramsey sum over states (SOS) approximation, it is demonstrated that the sign and magnitude of the paramagnetic contribution to the shielding correlates well with the observed 11 B chemical shifts of a substantial variety of boron- and metal-containing compounds. Analysis of the molecular orbital (MO) contributions in the SOS approximation leads to an explanation of the large downfield shifts associated with metal-rich metallaboranes. A similar analysis demonstrates the importance of selected cluster occupied and unoccupied MO's in explaining both exo-cage substituent effects in which the antipodal boron resonance is shifted upfield and endo-cage substituent effects (interchange of isolobal fragments within the cage framework) in which the antipodal boron resonance is shifted downfield. Exo- and endo-cage substitution perturbs these MO's in an understandable fashion, leading to an internally consistent explanation of the observed chemical shift changes. 36 refs., 8 figs., 4 tabs

An effective trust-based recommendation method using a novel graph clustering algorithm

Science.gov (United States)

Moradi, Parham; Ahmadian, Sajad; Akhlaghian, Fardin

2015-10-01

Recommender systems are programs that aim to provide personalized recommendations to users for specific items (e.g. music, books) in online sharing communities or on e-commerce sites. Collaborative filtering methods are important and widely accepted types of recommender systems that generate recommendations based on the ratings of like-minded users. On the other hand, these systems confront several inherent issues such as data sparsity and cold start problems, caused by fewer ratings against the unknowns that need to be predicted. Incorporating trust information into the collaborative filtering systems is an attractive approach to resolve these problems. In this paper, we present a model-based collaborative filtering method by applying a novel graph clustering algorithm and also considering trust statements. In the proposed method first of all, the problem space is represented as a graph and then a sparsest subgraph finding algorithm is applied on the graph to find the initial cluster centers. Then, the proposed graph clustering algorithm is performed to obtain the appropriate users/items clusters. Finally, the identified clusters are used as a set of neighbors to recommend unseen items to the current active user. Experimental results based on three real-world datasets demonstrate that the proposed method outperforms several state-of-the-art recommender system methods.

Differences Between Ward's and UPGMA Methods of Cluster Analysis: Implications for School Psychology.

Science.gov (United States)

Hale, Robert L.; Dougherty, Donna

1988-01-01

Compared the efficacy of two methods of cluster analysis, the unweighted pair-groups method using arithmetic averages (UPGMA) and Ward's method, for students grouped on intelligence, achievement, and social adjustment by both clustering methods. Found UPGMA more efficacious based on output, on cophenetic correlation coefficients generated by each…

The Detailed Chemical Properties of M31 Star Clusters. I. Fe, Alpha and Light Elements

Science.gov (United States)

Colucci, Janet E.; Bernstein, Rebecca A.; Cohen, Judith G.

2014-12-01

We present ages, [Fe/H] and abundances of the α elements Ca I, Si I, Ti I, Ti II, and light elements Mg I, Na I, and Al I for 31 globular clusters (GCs) in M31, which were obtained from high-resolution, high signal-to-noise ratio >60 echelle spectra of their integrated light (IL). All abundances and ages are obtained using our original technique for high-resolution IL abundance analysis of GCs. This sample provides a never before seen picture of the chemical history of M31. The GCs are dispersed throughout the inner and outer halo, from 2.5 kpc < R M31 < 117 kpc. We find a range of [Fe/H] within 20 kpc of the center of M31, and a constant [Fe/H] ~ - 1.6 for the outer halo clusters. We find evidence for at least one massive GC in M31 with an age between 1 and 5 Gyr. The α-element ratios are generally similar to the Milky Way GC and field star ratios. We also find chemical evidence for a late-time accretion origin for at least one cluster, which has a different abundance pattern than other clusters at similar metallicity. We find evidence for star-to-star abundance variations in Mg, Na, and Al in the GCs in our sample, and find correlations of Ca, Mg, Na, and possibly Al abundance ratios with cluster luminosity and velocity dispersion, which can potentially be used to constrain GC self-enrichment scenarios. Data presented here were obtained with the HIRES echelle spectrograph on the Keck I telescope. The data presented herein were obtained at the W. M. Keck Observatory, which is operated as a scientific partnership among the California Institute of Technology, the University of California and the National Aeronautics and Space Administration. The Observatory was made possible by the generous financial support of the W. M. Keck Foundation.

A dynamic lattice searching method with rotation operation for optimization of large clusters

International Nuclear Information System (INIS)

Wu Xia; Cai Wensheng; Shao Xueguang

2009-01-01

Global optimization of large clusters has been a difficult task, though much effort has been paid and many efficient methods have been proposed. During our works, a rotation operation (RO) is designed to realize the structural transformation from decahedra to icosahedra for the optimization of large clusters, by rotating the atoms below the center atom with a definite degree around the fivefold axis. Based on the RO, a development of the previous dynamic lattice searching with constructed core (DLSc), named as DLSc-RO, is presented. With an investigation of the method for the optimization of Lennard-Jones (LJ) clusters, i.e., LJ 500 , LJ 561 , LJ 600 , LJ 665-667 , LJ 670 , LJ 685 , and LJ 923 , Morse clusters, silver clusters by Gupta potential, and aluminum clusters by NP-B potential, it was found that both the global minima with icosahedral and decahedral motifs can be obtained, and the method is proved to be efficient and universal.

Chemical Ligation: A Versatile Method for Nucleoside Modification with Boron Clusters

Czech Academy of Sciences Publication Activity Database

Wojtczak, B. A.; Andrysiak, A.; Grüner, Bohumír; Lesnikowski, Z. J.

-, č. 14 (2008), s. 10675-10682 ISSN 0947-6539 R&D Projects: GA MŠk LC523 Grant - others:MSHE(PL) N405 051 32/3592; MSHE(PL) K152/H03/2007/10 Institutional research plan: CEZ:AV0Z40320502 Keywords : alkynes * azides * chemical ligation Subject RIV: CA - Inorganic Chemistry Impact factor: 5.454, year: 2008

Clustering method to process signals from a CdZnTe detector

International Nuclear Information System (INIS)

Zhang, Lan; Takahashi, Hiroyuki; Fukuda, Daiji; Nakazawa, Masaharu

2001-01-01

The poor mobility of holes in a compound semiconductor detector results in the imperfect collection of the primary charge deposited in the detector. Furthermore the fluctuation of the charge loss efficiency due to the change in the hole collection path length seriously degrades the energy resolution of the detector. Since the charge collection efficiency varies with the signal waveform, we can expect the improvement of the energy resolution through a proper waveform signal processing method. We developed a new digital signal processing technique, a clustering method which derives typical patterns containing the information on the real situation inside a detector from measured signals. The obtained typical patterns for the detector are then used for the pattern matching method. Measured signals are classified through analyzing the practical waveform variation due to the charge trapping, the electric field and the crystal defect etc. Signals with similar shape are placed into the same cluster. For each cluster we calculate an average waveform as a reference pattern. Using these reference patterns obtained from all the clusters, we can classify other measured signal waveforms from the same detector. Then signals are independently processed according to the classified category and form corresponding spectra. Finally these spectra are merged into one spectrum by multiplying normalization coefficients. The effectiveness of this method was verified with a CdZnTe detector of 2 mm thick and a 137 Cs gamma-ray source. The obtained energy resolution as improved to about 8 keV (FWHM). Because the clustering method is only related to the measured waveforms, it can be applied to any type and size of detectors and compatible with any type of filtering methods. (author)

AN EFFICIENT INITIALIZATION METHOD FOR K-MEANS CLUSTERING OF HYPERSPECTRAL DATA

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A. Alizade Naeini

2014-10-01

Full Text Available K-means is definitely the most frequently used partitional clustering algorithm in the remote sensing community. Unfortunately due to its gradient decent nature, this algorithm is highly sensitive to the initial placement of cluster centers. This problem deteriorates for the high-dimensional data such as hyperspectral remotely sensed imagery. To tackle this problem, in this paper, the spectral signatures of the endmembers in the image scene are extracted and used as the initial positions of the cluster centers. For this purpose, in the first step, A Neyman–Pearson detection theory based eigen-thresholding method (i.e., the HFC method has been employed to estimate the number of endmembers in the image. Afterwards, the spectral signatures of the endmembers are obtained using the Minimum Volume Enclosing Simplex (MVES algorithm. Eventually, these spectral signatures are used to initialize the k-means clustering algorithm. The proposed method is implemented on a hyperspectral dataset acquired by ROSIS sensor with 103 spectral bands over the Pavia University campus, Italy. For comparative evaluation, two other commonly used initialization methods (i.e., Bradley & Fayyad (BF and Random methods are implemented and compared. The confusion matrix, overall accuracy and Kappa coefficient are employed to assess the methods’ performance. The evaluations demonstrate that the proposed solution outperforms the other initialization methods and can be applied for unsupervised classification of hyperspectral imagery for landcover mapping.

A semi-supervised method to detect seismic random noise with fuzzy GK clustering

International Nuclear Information System (INIS)

Hashemi, Hosein; Javaherian, Abdolrahim; Babuska, Robert

2008-01-01

We present a new method to detect random noise in seismic data using fuzzy Gustafson–Kessel (GK) clustering. First, using an adaptive distance norm, a matrix is constructed from the observed seismic amplitudes. The next step is to find centres of ellipsoidal clusters and construct a partition matrix which determines the soft decision boundaries between seismic events and random noise. The GK algorithm updates the cluster centres in order to iteratively minimize the cluster variance. Multiplication of the fuzzy membership function with values of each sample yields new sections; we name them 'clustered sections'. The seismic amplitude values of the clustered sections are given in a way to decrease the level of noise in the original noisy seismic input. In pre-stack data, it is essential to study the clustered sections in a f–k domain; finding the quantitative index for weighting the post-stack data needs a similar approach. Using the knowledge of a human specialist together with the fuzzy unsupervised clustering, the method is a semi-supervised random noise detection. The efficiency of this method is investigated on synthetic and real seismic data for both pre- and post-stack data. The results show a significant improvement of the input noisy sections without harming the important amplitude and phase information of the original data. The procedure for finding the final weights of each clustered section should be carefully done in order to keep almost all the evident seismic amplitudes in the output section. The method interactively uses the knowledge of the seismic specialist in detecting the noise

Comparison of Chemical and Physical-chemical Wastewater Discoloring Methods

Directory of Open Access Journals (Sweden)

Durašević, V.

2007-11-01

Full Text Available Today's chemical and physical-chemical wastewater discoloration methods do not completely meet demands regarding degree of discoloration. In this paper discoloration was performed using Fenton (FeSO4 . 7 H2O + H2O2 + H2SO4 and Fenton-like (FeCl3 . 6 H2O + H2O2 + HCOOH chemical methods and physical-chemical method of coagulation/flocculation (using poly-electrolyte (POEL combining anion active coagulant (modified poly-acrylamides and cationic flocculant (product of nitrogen compounds in combination with adsorption on activated carbon. Suitability of aforementioned methods was investigated on reactive and acid dyes, regarding their most common use in the textile industry. Also, investigations on dyes of different chromogen (anthraquinone, phthalocyanine, azo and xanthene were carried out in order to determine the importance of molecular spatial structure. Oxidative effect of Fenton and Fenton-like reagents resulted in decomposition of colored chromogen and high degree of discoloration. However, the problem is the inability of adding POEL in stechiometrical ratio (also present in physical-chemical methods, when the phenomenon of overdosing coagulants occurs in order to obtain a higher degree of discoloration, creating a potential danger of burdening water with POEL. Input and output water quality was controlled through spectrophotometric measurements and standard biological parameters. In addition, part of the investigations concerned industrial wastewaters obtained from dyeing cotton materials using reactive dye (C. I. Reactive Blue 19, a process that demands the use of vast amounts of electrolytes. Also, investigations of industrial wastewaters was labeled as a crucial step carried out in order to avoid serious misassumptions and false conclusions, which may arise if dyeing processes are only simulated in the laboratory.

Method for Determining Appropriate Clustering Criteria of Location-Sensing Data

Directory of Open Access Journals (Sweden)

Youngmin Lee

2016-08-01

Full Text Available Large quantities of location-sensing data are generated from location-based social network services. These data are provided as point properties with location coordinates acquired from a global positioning system or Wi-Fi signal. To show the point data on multi-scale map services, the data should be represented by clusters following a grid-based clustering method, in which an appropriate grid size should be determined. Currently, there are no criteria for determining the proper grid size, and the modifiable areal unit problem has been formulated for the purpose of addressing this issue. The method proposed in this paper is applies a hexagonal grid to geotagged Twitter point data, considering the grid size in terms of both quantity and quality to minimize the limitations associated with the modifiable areal unit problem. Quantitatively, we reduced the original Twitter point data by an appropriate amount using Töpfer’s radical law. Qualitatively, we maintained the original distribution characteristics using Moran’s I. Finally, we determined the appropriate sizes of clusters from zoom levels 9–13 by analyzing the distribution of data on the graphs. Based on the visualized clustering results, we confirm that the original distribution pattern is effectively maintained using the proposed method.

Chemical Abundances of Red Giant Stars in the Globular Cluster M107 (NGC 6171)

Science.gov (United States)

O'Connell, Julia E.; Johnson, Christian I.; Pilachowski, Catherine A.; Burks, Geoffrey

2011-10-01

We present chemical abundances of Al and several Fe-Peak and neutron-capture elements for 13 red giant branch stars in the Galactic globular cluster NGC 6171 (M107). The abundances were determined using equivalent width and spectrum synthesis analyses of moderate-resolution ( R ˜ 15,000), moderate signal-to-noise ratio ( ˜ 80) spectra obtained with the WIYN telescope and Hydra multifiber spectrograph. A comparison between photometric and spectroscopic effective temperature estimates seems to indicate that a reddening value of E(B - V) = 0.46 may be more appropriate for this cluster than the more commonly used value of E(B - V) = 0.33. Similarly, we found that a distance modulus of (m - M)V ≈ 13.7 provided reasonable surface gravity estimates for the stars in our sample. Our spectroscopic analysis finds M107 to be moderately metal-poor with = -0.93 and also exhibits a small star-to-star metallicity dispersion (σ = 0.04). These results are consistent with previous photometric and spectroscopic studies. Aluminum appears to be moderately enhanced in all program stars ( = +0.39, σ = 0.11). The relatively small star-to-star scatter in [Al/Fe] differs from the trend found in more metal-poor globular clusters, and is more similar to what is found in clusters with [Fe/H] ≳ -1. The cluster also appears to be moderately r-process-enriched with = +0.32 (σ = 0.17).

Model reduction of detailed-balanced reaction networks by clustering linkage classes

NARCIS (Netherlands)

Rao, Shodhan; Jayawardhana, Bayu; van der Schaft, Abraham; Findeisen, Rolf; Bullinger, Eric; Balsa-Canto, Eva; Bernaerts, Kristel

2016-01-01

We propose a model reduction method that involves sequential application of clustering of linkage classes and Kron reduction. This approach is specifically useful for chemical reaction networks with each linkage class having less number of reactions. In case of detailed balanced chemical reaction

A cluster approximation for the transfer-matrix method

International Nuclear Information System (INIS)

Surda, A.

1990-08-01

A cluster approximation for the transfer-method is formulated. The calculation of the partition function of lattice models is transformed to a nonlinear mapping problem. The method yields the free energy, correlation functions and the phase diagrams for a large class of lattice models. The high accuracy of the method is exemplified by the calculation of the critical temperature of the Ising model. (author). 14 refs, 2 figs, 1 tab

Hydration structure and dynamics of a hydroxide ion in water clusters of varying size and temperature: Quantum chemical and ab initio molecular dynamics studies

International Nuclear Information System (INIS)

Bankura, Arindam; Chandra, Amalendu

2012-01-01

Highlights: ► A theoretical study of hydroxide ion-water clusters is carried for varying cluster size and temperature. ► The structures of OH − (H 2 O) n are found out through quantum chemical calculations for n = 4, 8, 16 and 20. ► The finite temperature behavior of the clusters is studied through ab initio dynamical simulations. ► The spectral features of OH modes (deuterated) and their dependence on hydrogen bonding states of water are discussed. ► The mechanism and kinetics of proton transfer processes in these anionic clusters are also investigated. - Abstract: We have investigated the hydration structure and dynamics of OH − (H 2 O) n clusters (n = 4, 8, 16 and 20) by means of quantum chemical and ab initio molecular dynamics calculations. Quantum chemical calculations reveal that the solvation structure of the hydroxide ion transforms from three and four-coordinated surface states to five-coordinated interior state with increase in cluster size. Several other isomeric structures with energies not very different from the most stable isomer are also found. Ab initio simulations show that the most probable configurations at higher temperatures need not be the lowest energy isomeric structure. The rates of proton transfer in these clusters are found to be slower than that in bulk water. The vibrational spectral calculations reveal distinct features for free OH (deuterated) stretch modes of water in different hydrogen bonding states. Effects of temperature on the structural and dynamical properties are also investigated for the largest cluster considered here.

Interactive K-Means Clustering Method Based on User Behavior for Different Analysis Target in Medicine.

Science.gov (United States)

Lei, Yang; Yu, Dai; Bin, Zhang; Yang, Yang

2017-01-01

Clustering algorithm as a basis of data analysis is widely used in analysis systems. However, as for the high dimensions of the data, the clustering algorithm may overlook the business relation between these dimensions especially in the medical fields. As a result, usually the clustering result may not meet the business goals of the users. Then, in the clustering process, if it can combine the knowledge of the users, that is, the doctor's knowledge or the analysis intent, the clustering result can be more satisfied. In this paper, we propose an interactive K -means clustering method to improve the user's satisfactions towards the result. The core of this method is to get the user's feedback of the clustering result, to optimize the clustering result. Then, a particle swarm optimization algorithm is used in the method to optimize the parameters, especially the weight settings in the clustering algorithm to make it reflect the user's business preference as possible. After that, based on the parameter optimization and adjustment, the clustering result can be closer to the user's requirement. Finally, we take an example in the breast cancer, to testify our method. The experiments show the better performance of our algorithm.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang

2017-02-16

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

Efficient nonparametric and asymptotic Bayesian model selection methods for attributed graph clustering

KAUST Repository

Xu, Zhiqiang; Cheng, James; Xiao, Xiaokui; Fujimaki, Ryohei; Muraoka, Yusuke

2017-01-01

Attributed graph clustering, also known as community detection on attributed graphs, attracts much interests recently due to the ubiquity of attributed graphs in real life. Many existing algorithms have been proposed for this problem, which are either distance based or model based. However, model selection in attributed graph clustering has not been well addressed, that is, most existing algorithms assume the cluster number to be known a priori. In this paper, we propose two efficient approaches for attributed graph clustering with automatic model selection. The first approach is a popular Bayesian nonparametric method, while the second approach is an asymptotic method based on a recently proposed model selection criterion, factorized information criterion. Experimental results on both synthetic and real datasets demonstrate that our approaches for attributed graph clustering with automatic model selection significantly outperform the state-of-the-art algorithm.

An improved K-means clustering method for cDNA microarray image segmentation.

Science.gov (United States)

Wang, T N; Li, T J; Shao, G F; Wu, S X

2015-07-14

Microarray technology is a powerful tool for human genetic research and other biomedical applications. Numerous improvements to the standard K-means algorithm have been carried out to complete the image segmentation step. However, most of the previous studies classify the image into two clusters. In this paper, we propose a novel K-means algorithm, which first classifies the image into three clusters, and then one of the three clusters is divided as the background region and the other two clusters, as the foreground region. The proposed method was evaluated on six different data sets. The analyses of accuracy, efficiency, expression values, special gene spots, and noise images demonstrate the effectiveness of our method in improving the segmentation quality.

Expanding Comparative Literature into Comparative Sciences Clusters with Neutrosophy and Quad-stage Method

Directory of Open Access Journals (Sweden)

Fu Yuhua

2016-08-01

Full Text Available By using Neutrosophy and Quad-stage Method, the expansions of comparative literature include: comparative social sciences clusters, comparative natural sciences clusters, comparative interdisciplinary sciences clusters, and so on. Among them, comparative social sciences clusters include: comparative literature, comparative history, comparative philosophy, and so on; comparative natural sciences clusters include: comparative mathematics, comparative physics, comparative chemistry, comparative medicine, comparative biology, and so on.

Analytical Energy Gradients for Excited-State Coupled-Cluster Methods

Science.gov (United States)

Wladyslawski, Mark; Nooijen, Marcel

The equation-of-motion coupled-cluster (EOM-CC) and similarity transformed equation-of-motion coupled-cluster (STEOM-CC) methods have been firmly established as accurate and routinely applicable extensions of single-reference coupled-cluster theory to describe electronically excited states. An overview of these methods is provided, with emphasis on the many-body similarity transform concept that is the key to a rationalization of their accuracy. The main topic of the paper is the derivation of analytical energy gradients for such non-variational electronic structure approaches, with an ultimate focus on obtaining their detailed algebraic working equations. A general theoretical framework using Lagrange's method of undetermined multipliers is presented, and the method is applied to formulate the EOM-CC and STEOM-CC gradients in abstract operator terms, following the previous work in [P.G. Szalay, Int. J. Quantum Chem. 55 (1995) 151] and [S.R. Gwaltney, R.J. Bartlett, M. Nooijen, J. Chem. Phys. 111 (1999) 58]. Moreover, the systematics of the Lagrange multiplier approach is suitable for automation by computer, enabling the derivation of the detailed derivative equations through a standardized and direct procedure. To this end, we have developed the SMART (Symbolic Manipulation and Regrouping of Tensors) package of automated symbolic algebra routines, written in the Mathematica programming language. The SMART toolkit provides the means to expand, differentiate, and simplify equations by manipulation of the detailed algebraic tensor expressions directly. The Lagrangian multiplier formulation establishes a uniform strategy to perform the automated derivation in a standardized manner: A Lagrange multiplier functional is constructed from the explicit algebraic equations that define the energy in the electronic method; the energy functional is then made fully variational with respect to all of its parameters, and the symbolic differentiations directly yield the explicit

Comparison of Bayesian clustering and edge detection methods for inferring boundaries in landscape genetics

Science.gov (United States)

Safner, T.; Miller, M.P.; McRae, B.H.; Fortin, M.-J.; Manel, S.

2011-01-01

Recently, techniques available for identifying clusters of individuals or boundaries between clusters using genetic data from natural populations have expanded rapidly. Consequently, there is a need to evaluate these different techniques. We used spatially-explicit simulation models to compare three spatial Bayesian clustering programs and two edge detection methods. Spatially-structured populations were simulated where a continuous population was subdivided by barriers. We evaluated the ability of each method to correctly identify boundary locations while varying: (i) time after divergence, (ii) strength of isolation by distance, (iii) level of genetic diversity, and (iv) amount of gene flow across barriers. To further evaluate the methods' effectiveness to detect genetic clusters in natural populations, we used previously published data on North American pumas and a European shrub. Our results show that with simulated and empirical data, the Bayesian spatial clustering algorithms outperformed direct edge detection methods. All methods incorrectly detected boundaries in the presence of strong patterns of isolation by distance. Based on this finding, we support the application of Bayesian spatial clustering algorithms for boundary detection in empirical datasets, with necessary tests for the influence of isolation by distance. ?? 2011 by the authors; licensee MDPI, Basel, Switzerland.

Trend analysis using non-stationary time series clustering based on the finite element method

Science.gov (United States)

Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

2014-05-01

In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods that can analyze multidimensional time series. One important attribute of this method is that it is not dependent on any statistical assumption and does not need local stationarity in the time series. In this paper, it is shown how the FEM-clustering method can be used to locate change points in the trend of temperature time series from in situ observations. This method is applied to the temperature time series of North Carolina (NC) and the results represent region-specific climate variability despite higher frequency harmonics in climatic time series. Next, we investigated the relationship between the climatic indices with the clusters/trends detected based on this clustering method. It appears that the natural variability of climate change in NC during 1950-2009 can be explained mostly by AMO and solar activity.

Time-of-flight secondary ion mass spectrometry with energetic cluster ion impact ionization for highly sensitive chemical structure characterization

Energy Technology Data Exchange (ETDEWEB)

Hirata, K., E-mail: k.hirata@aist.go.jp [National Metrology Institute of Japan, National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Higashi, Tsukuba, Ibaraki 305-8565 (Japan); Saitoh, Y.; Chiba, A.; Yamada, K.; Narumi, K. [Takasaki Advanced Radiation Research Institute (TARRI), Japan Atomic Energy Agency (JAEA), Takasaki, Gumma 370-1292 (Japan)

2013-11-01

Energetic cluster ions with energies of the order of sub MeV or greater were applied to time-of-flight (TOF) secondary ion (SI) mass spectrometry. This gave various advantages including enhancement of SIs required for chemical structure characterization and prevention of charging effects in SI mass spectra for organic targets. We report some characteristic features of TOF SI mass spectrometry using energetic cluster ion impact ionization and discuss two future applications of it.

Robustness of serial clustering of extratropical cyclones to the choice of tracking method

Directory of Open Access Journals (Sweden)

Joaquim G. Pinto

2016-07-01

Full Text Available Cyclone clusters are a frequent synoptic feature in the Euro-Atlantic area. Recent studies have shown that serial clustering of cyclones generally occurs on both flanks and downstream regions of the North Atlantic storm track, while cyclones tend to occur more regulary on the western side of the North Atlantic basin near Newfoundland. This study explores the sensitivity of serial clustering to the choice of cyclone tracking method using cyclone track data from 15 methods derived from ERA-Interim data (1979–2010. Clustering is estimated by the dispersion (ratio of variance to mean of winter [December – February (DJF] cyclone passages near each grid point over the Euro-Atlantic area. The mean number of cyclone counts and their variance are compared between methods, revealing considerable differences, particularly for the latter. Results show that all different tracking methods qualitatively capture similar large-scale spatial patterns of underdispersion and overdispersion over the study region. The quantitative differences can primarily be attributed to the differences in the variance of cyclone counts between the methods. Nevertheless, overdispersion is statistically significant for almost all methods over parts of the eastern North Atlantic and Western Europe, and is therefore considered as a robust feature. The influence of the North Atlantic Oscillation (NAO on cyclone clustering displays a similar pattern for all tracking methods, with one maximum near Iceland and another between the Azores and Iberia. The differences in variance between methods are not related with different sensitivities to the NAO, which can account to over 50% of the clustering in some regions. We conclude that the general features of underdispersion and overdispersion of extratropical cyclones over the North Atlantic and Western Europe are robust to the choice of tracking method. The same is true for the influence of the NAO on cyclone dispersion.

Measuring age differences among globular clusters having similar metallicities - A new method and first results

International Nuclear Information System (INIS)

Vandenberg, D.A.; Bolte, M.; Stetson, P.B.

1990-01-01

A color-difference technique for estimating the relative ages of globular clusters with similar chemical compositions on the basis of their CM diagrams is described and demonstrated. The theoretical basis and implementation of the procedure are explained, and results for groups of globular clusters with m/H = about -2, -1.6, and -1.3, and for two special cases (Palomar 12 and NGC 5139) are presented in extensive tables and graphs and discussed in detail. It is found that the more metal-deficient globular clusters are nearly coeval (differences less than 0.5 Gyr), whereas the most metal-rich globular clusters exhibit significant age differences (about 2 Gyr). This result is shown to contradict Galactic evolution models postulating halo collapse in less than a few times 100 Myr. 77 refs

Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

Directory of Open Access Journals (Sweden)

I. Crawford

2015-11-01

Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

Multiple-Features-Based Semisupervised Clustering DDoS Detection Method

Directory of Open Access Journals (Sweden)

Yonghao Gu

2017-01-01

Full Text Available DDoS attack stream from different agent host converged at victim host will become very large, which will lead to system halt or network congestion. Therefore, it is necessary to propose an effective method to detect the DDoS attack behavior from the massive data stream. In order to solve the problem that large numbers of labeled data are not provided in supervised learning method, and the relatively low detection accuracy and convergence speed of unsupervised k-means algorithm, this paper presents a semisupervised clustering detection method using multiple features. In this detection method, we firstly select three features according to the characteristics of DDoS attacks to form detection feature vector. Then, Multiple-Features-Based Constrained-K-Means (MF-CKM algorithm is proposed based on semisupervised clustering. Finally, using MIT Laboratory Scenario (DDoS 1.0 data set, we verify that the proposed method can improve the convergence speed and accuracy of the algorithm under the condition of using a small amount of labeled data sets.

Cluster cosmological analysis with X ray instrumental observables: introduction and testing of AsPIX method

International Nuclear Information System (INIS)

Valotti, Andrea

2016-01-01

Cosmology is one of the fundamental pillars of astrophysics, as such it contains many unsolved puzzles. To investigate some of those puzzles, we analyze X-ray surveys of galaxy clusters. These surveys are possible thanks to the bremsstrahlung emission of the intra-cluster medium. The simultaneous fit of cluster counts as a function of mass and distance provides an independent measure of cosmological parameters such as Ω m , σ s , and the dark energy equation of state w0. A novel approach to cosmological analysis using galaxy cluster data, called top-down, was developed in N. Clerc et al. (2012). This top-down approach is based purely on instrumental observables that are considered in a two-dimensional X-ray color-magnitude diagram. The method self-consistently includes selection effects and scaling relationships. It also provides a means of bypassing the computation of individual cluster masses. My work presents an extension of the top-down method by introducing the apparent size of the cluster, creating a three-dimensional X-ray cluster diagram. The size of a cluster is sensitive to both the cluster mass and its angular diameter, so it must also be included in the assessment of selection effects. The performance of this new method is investigated using a Fisher analysis. In parallel, I have studied the effects of the intrinsic scatter in the cluster size scaling relation on the sample selection as well as on the obtained cosmological parameters. To validate the method, I estimate uncertainties of cosmological parameters with MCMC method Amoeba minimization routine and using two simulated XMM surveys that have an increasing level of complexity. The first simulated survey is a set of toy catalogues of 100 and 10000 deg 2 , whereas the second is a 1000 deg 2 catalogue that was generated using an Aardvark semi-analytical N-body simulation. This comparison corroborates the conclusions of the Fisher analysis. In conclusion, I find that a cluster diagram that accounts

Unbiased methods for removing systematics from galaxy clustering measurements

Science.gov (United States)

Elsner, Franz; Leistedt, Boris; Peiris, Hiranya V.

2016-02-01

Measuring the angular clustering of galaxies as a function of redshift is a powerful method for extracting information from the three-dimensional galaxy distribution. The precision of such measurements will dramatically increase with ongoing and future wide-field galaxy surveys. However, these are also increasingly sensitive to observational and astrophysical contaminants. Here, we study the statistical properties of three methods proposed for controlling such systematics - template subtraction, basic mode projection, and extended mode projection - all of which make use of externally supplied template maps, designed to characterize and capture the spatial variations of potential systematic effects. Based on a detailed mathematical analysis, and in agreement with simulations, we find that the template subtraction method in its original formulation returns biased estimates of the galaxy angular clustering. We derive closed-form expressions that should be used to correct results for this shortcoming. Turning to the basic mode projection algorithm, we prove it to be free of any bias, whereas we conclude that results computed with extended mode projection are biased. Within a simplified setup, we derive analytical expressions for the bias and discuss the options for correcting it in more realistic configurations. Common to all three methods is an increased estimator variance induced by the cleaning process, albeit at different levels. These results enable unbiased high-precision clustering measurements in the presence of spatially varying systematics, an essential step towards realizing the full potential of current and planned galaxy surveys.

A New Soft Computing Method for K-Harmonic Means Clustering.

Science.gov (United States)

Yeh, Wei-Chang; Jiang, Yunzhi; Chen, Yee-Fen; Chen, Zhe

2016-01-01

The K-harmonic means clustering algorithm (KHM) is a new clustering method used to group data such that the sum of the harmonic averages of the distances between each entity and all cluster centroids is minimized. Because it is less sensitive to initialization than K-means (KM), many researchers have recently been attracted to studying KHM. In this study, the proposed iSSO-KHM is based on an improved simplified swarm optimization (iSSO) and integrates a variable neighborhood search (VNS) for KHM clustering. As evidence of the utility of the proposed iSSO-KHM, we present extensive computational results on eight benchmark problems. From the computational results, the comparison appears to support the superiority of the proposed iSSO-KHM over previously developed algorithms for all experiments in the literature.

Are clusters important in understanding the mechanisms in atmospheric pressure ionization? Part 1: Reagent ion generation and chemical control of ion populations.

Science.gov (United States)

Klee, Sonja; Derpmann, Valerie; Wißdorf, Walter; Klopotowski, Sebastian; Kersten, Hendrik; Brockmann, Klaus J; Benter, Thorsten; Albrecht, Sascha; Bruins, Andries P; Dousty, Faezeh; Kauppila, Tiina J; Kostiainen, Risto; O'Brien, Rob; Robb, Damon B; Syage, Jack A

2014-08-01

It is well documented since the early days of the development of atmospheric pressure ionization methods, which operate in the gas phase, that cluster ions are ubiquitous. This holds true for atmospheric pressure chemical ionization, as well as for more recent techniques, such as atmospheric pressure photoionization, direct analysis in real time, and many more. In fact, it is well established that cluster ions are the primary carriers of the net charge generated. Nevertheless, cluster ion chemistry has only been sporadically included in the numerous proposed ionization mechanisms leading to charged target analytes, which are often protonated molecules. This paper series, consisting of two parts, attempts to highlight the role of cluster ion chemistry with regard to the generation of analyte ions. In addition, the impact of the changing reaction matrix and the non-thermal collisions of ions en route from the atmospheric pressure ion source to the high vacuum analyzer region are discussed. This work addresses such issues as extent of protonation versus deuteration, the extent of analyte fragmentation, as well as highly variable ionization efficiencies, among others. In Part 1, the nature of the reagent ion generation is examined, as well as the extent of thermodynamic versus kinetic control of the resulting ion population entering the analyzer region.

Prediction of Solvent Physical Properties using the Hierarchical Clustering Method

Science.gov (United States)

Recently a QSAR (Quantitative Structure Activity Relationship) method, the hierarchical clustering method, was developed to estimate acute toxicity values for large, diverse datasets. This methodology has now been applied to the estimate solvent physical properties including sur...

A quasiparticle-based multi-reference coupled-cluster method.

Science.gov (United States)

Rolik, Zoltán; Kállay, Mihály

2014-10-07

The purpose of this paper is to introduce a quasiparticle-based multi-reference coupled-cluster (MRCC) approach. The quasiparticles are introduced via a unitary transformation which allows us to represent a complete active space reference function and other elements of an orthonormal multi-reference (MR) basis in a determinant-like form. The quasiparticle creation and annihilation operators satisfy the fermion anti-commutation relations. On the basis of these quasiparticles, a generalization of the normal-ordered operator products for the MR case can be introduced as an alternative to the approach of Mukherjee and Kutzelnigg [Recent Prog. Many-Body Theor. 4, 127 (1995); Mukherjee and Kutzelnigg, J. Chem. Phys. 107, 432 (1997)]. Based on the new normal ordering any quasiparticle-based theory can be formulated using the well-known diagram techniques. Beyond the general quasiparticle framework we also present a possible realization of the unitary transformation. The suggested transformation has an exponential form where the parameters, holding exclusively active indices, are defined in a form similar to the wave operator of the unitary coupled-cluster approach. The definition of our quasiparticle-based MRCC approach strictly follows the form of the single-reference coupled-cluster method and retains several of its beneficial properties. Test results for small systems are presented using a pilot implementation of the new approach and compared to those obtained by other MR methods.

Cluster analysis of European Y-chromosomal STR haplotypes using the discrete Laplace method

DEFF Research Database (Denmark)

Andersen, Mikkel Meyer; Eriksen, Poul Svante; Morling, Niels

2014-01-01

The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models the probabi......The European Y-chromosomal short tandem repeat (STR) haplotype distribution has previously been analysed in various ways. Here, we introduce a new way of analysing population substructure using a new method based on clustering within the discrete Laplace exponential family that models...... the probability distribution of the Y-STR haplotypes. Creating a consistent statistical model of the haplotypes enables us to perform a wide range of analyses. Previously, haplotype frequency estimation using the discrete Laplace method has been validated. In this paper we investigate how the discrete Laplace...... method can be used for cluster analysis to further validate the discrete Laplace method. A very important practical fact is that the calculations can be performed on a normal computer. We identified two sub-clusters of the Eastern and Western European Y-STR haplotypes similar to results of previous...

Supersonic cluster beams: a powerful method for the deposition of nanostructured thin films with tailored properties

International Nuclear Information System (INIS)

Milani, P.

2002-01-01

By using a pulsed micro-plasma cluster source and by exploiting aero-dynamical effects typical of supersonic beams it is possible to obtain very high deposition rates with a control on neutral cluster mass distribution, allowing the deposition of thin films with controlled nanostructure. Due to high deposition rates, high lateral resolution, low temperature processing supersonic cluster beams can also be used for the micro and nano-patterning of cluster-assembled films when little or no post-growth manipulation or assembly is required. For example the nano and meso-structure of films obtained by carbon cluster beam deposition can be controlled by selecting in the beam the elemental building blocks, moreover functional properties such as field emission can be controlled and tailored. The use of supersonic cluster beams opens also new perspectives for the production of nano-structured films with novel physico-chemical and topological properties such as nano-structured carbon matrices containing carbide and transition metal particles. (Author)

Dynamics, Chemical Abundances, and ages of Globular Clusters in the Virgo Cluster of Galaxies

Science.gov (United States)

Guhathakurta, Puragra; NGVS Collaboration

2018-01-01

We present a study of the dynamics, metallicities, and ages of globular clusters (GCs) in the Next Generation Virgo cluster Survey (NGVS), a deep, multi-band (u, g, r, i, z, and Ks), wide-field (104 deg2) imaging survey carried out using the 3.6-m Canada-France-Hawaii Telescope and MegaCam imager. GC candidates were selected from the NGVS survey using photometric and image morphology criteria and these were followed up with deep, medium-resolution, multi-object spectroscopy using the Keck II 10-m telescope and DEIMOS spectrograph. The primary spectroscopic targets were candidate GC satellites of dwarf elliptical (dE) and ultra-diffuse galaxies (UDGs) in the Virgo cluster. While many objects were confirmed as GC satellites of Virgo dEs and UDGs, many turned out to be non-satellites based on their radial velocity and/or positional mismatch any identifiable Virgo cluster galaxy. We have used a combination of spectral characteristics (e.g., presence of absorption vs. emission lines), new Gaussian mixture modeling of radial velocity and sky position data, and a new extreme deconvolution analysis of ugrizKs photometry and image morphology, to classify all the objects in our sample into: (1) GC satellites of dE galaxies, (2) GC satellites of UDGs, (3) intra-cluster GCs (ICGCs) in the Virgo cluster, (4) GCs in the outer halo of the central cluster galaxy M87, (5) foreground Milky Way stars, and (6) distant background galaxies. We use these data to study the dynamics and dark matter content of dE and UDGs in the Virgo cluster, place important constraints on the nature of dE nuclei, and study the origin of ICGCs versus GCs in the remote M87 halo.We are grateful for financial support from the NSF and NASA/STScI.

A method of clustering observers with different visual characteristics

Energy Technology Data Exchange (ETDEWEB)

Niimi, Takanaga [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Imai, Kuniharu [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Ikeda, Mitsuru [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan); Maeda, Hisatoshi [Nagoya University School of Health Sciences, Department of Radiological Technology, 1-1-20 Daiko-minami, Higashi-ku, Nagoya 461-8673 (Japan)

2006-01-15

Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control.

A method of clustering observers with different visual characteristics

International Nuclear Information System (INIS)

Niimi, Takanaga; Imai, Kuniharu; Ikeda, Mitsuru; Maeda, Hisatoshi

2006-01-01

Evaluation of observer's image perception in medical images is important, and yet has not been performed because it is difficult to quantify visual characteristics. In the present study, we investigated the observer's image perception by clustering a group of 20 observers. Images of a contrast-detail (C-D) phantom, which had cylinders of 10 rows and 10 columns with different diameters and lengths, were acquired with an X-ray screen-film system with fixed exposure conditions. A group of 10 films were prepared for visual evaluations. Sixteen radiological technicians, three radiologists and one medical physicist participated in the observation test. All observers read the phantom radiographs on a transillumination image viewer with room lights off. The detectability was defined as the shortest length of the cylinders of which border the observers could recognize from the background, and was recorded using the number of columns. The detectability was calculated as the average of 10 readings for each observer, and plotted for different phantom diameter. The unweighted pair-group method using arithmetic averages (UPGMA) was adopted for clustering. The observers were clustered into two groups: one group selected objects with a demarcation from the vicinity, and the other group searched for the objects with their eyes constrained. This study showed a usefulness of the cluster method to select personnel with the similar perceptual predisposition when a C-D phantom was used in image quality control

CHEMICAL ABUNDANCE EVIDENCE OF ENDURING HIGH STAR FORMATION RATES IN AN EARLY-TYPE GALAXY: HIGH [Ca/Fe] IN NGC 5128 GLOBULAR CLUSTERS

International Nuclear Information System (INIS)

Colucci, Janet E.; Durán, María Fernanda; Bernstein, Rebecca A.; McWilliam, Andrew

2013-01-01

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between –1.6 and –0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <–0.4 is +0.37 ± 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 ± 0.09 and +0.24 ± 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope

CHEMICAL ABUNDANCE EVIDENCE OF ENDURING HIGH STAR FORMATION RATES IN AN EARLY-TYPE GALAXY: HIGH [Ca/Fe] IN NGC 5128 GLOBULAR CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Colucci, Janet E.; Duran, Maria Fernanda; Bernstein, Rebecca A. [Department of Astronomy and Astrophysics, 1156 High Street, UCO/Lick Observatory, University of California, Santa Cruz, CA 95064 (United States); McWilliam, Andrew, E-mail: jcolucci@ucolick.org [Observatories of the Carnegie Institute of Washington, 813 Santa Barbara Street, Pasadena, CA 91101-1292 (United States)

2013-08-20

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between -1.6 and -0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <-0.4 is +0.37 {+-} 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 {+-} 0.09 and +0.24 {+-} 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope.

Electronic structure and properties of designer clusters and cluster-assemblies

International Nuclear Information System (INIS)

Khanna, S.N.; Jena, P.

1995-01-01

Using self-consistent calculations based on density functional theory, we demonstrate that electronic shell filling and close atomic packing criteria can be used to design ultra-stable clusters. Interaction of these clusters with each other and with gas atoms is found to be weak confirming their chemical inertness. A crystal composed of these inert clusters is expected to have electronic properties that are markedly different from crystals where atoms are the building blocks. The recent observation of ferromagnetism in potassium clusters assembled in zeolite cages is discussed. (orig.)

Spectroscopic Chemical Analysis Methods and Apparatus

Science.gov (United States)

Hug, William F. (Inventor); Reid, Ray D. (Inventor); Bhartia, Rohit (Inventor); Lane, Arthur L. (Inventor)

2018-01-01

Spectroscopic chemical analysis methods and apparatus are disclosed which employ deep ultraviolet (e.g. in the 200 nm to 300 nm spectral range) electron beam pumped wide bandgap semiconductor lasers, incoherent wide bandgap semiconductor light emitting devices, and hollow cathode metal ion lasers to perform non-contact, non-invasive detection of unknown chemical analytes. These deep ultraviolet sources enable dramatic size, weight and power consumption reductions of chemical analysis instruments. In some embodiments, Raman spectroscopic detection methods and apparatus use ultra-narrow-band angle tuning filters, acousto-optic tuning filters, and temperature tuned filters to enable ultra-miniature analyzers for chemical identification. In some embodiments Raman analysis is conducted along with photoluminescence spectroscopy (i.e. fluorescence and/or phosphorescence spectroscopy) to provide high levels of sensitivity and specificity in the same instrument.

Application of a Light-Front Coupled Cluster Method

International Nuclear Information System (INIS)

Chabysheva, S.S.; Hiller, J.R.

2012-01-01

As a test of the new light-front coupled-cluster method in a gauge theory, we apply it to the nonperturbative construction of the dressed-electron state in QED, for an arbitrary covariant gauge, and compute the electron's anomalous magnetic moment. The construction illustrates the spectator and Fock-sector independence of vertex and self-energy contributions and indicates resolution of the difficulties with uncanceled divergences that plague methods based on Fock-space truncation. (author)

Potential for supernova-induced chemical enrichment of protoglobular cluster clouds

International Nuclear Information System (INIS)

Dopita, M.A.; Smith, G.H.; Dominion Astrophysical Observatory, Victoria, Canada)

1986-01-01

This paper seeks to explain the large internal abundance variations that are seen in the globular cluster Omega Cen in terms of supernova-induced chemical enrichment that occurred when the cluster was still largely in a gaseous phase and star formation was continuing. Using a simple power-law density model of this protoglobular gas cloud, the conditions under which this can occur have been established analytically. Clouds less massive than about 100,000 solar masses are completely disrupted by supernova explosions in their adiabatic phase. In clouds of greater mass, supernova explosions occurring near the tidal radius tend to lose their hot gas and metals to the intercloud medium. For explosions occurring closer to the mass center the ejecta must be slowed below the escape velocity, and this can only occur in clouds more massive than about 3 x 10 to the 6th solar masses. If this condition is met, then the slow isothermal momentum-conserving shocks generated by the supernova explosions may eventually induce secondary star formation. For such shocks converging on the mass center, it is found that a cloud mass of at least 10 to the 7th solar masses is required for this process to be efficient. From the observed properties of Omega Cen, a primordial mass of order 10 to the 8th solar masses is estimated, which emphasizes the unusual character of this object. 39 references

CHEMICAL ABUNDANCES IN NGC 5053: A VERY METAL-POOR AND DYNAMICALLY COMPLEX GLOBULAR CLUSTER

Energy Technology Data Exchange (ETDEWEB)

Boberg, Owen M.; Friel, Eileen D.; Vesperini, Enrico [Astronomy Department, Indiana University, Bloomington, IN 47405 (United States)

2015-05-10

NGC 5053 provides a rich environment to test our understanding of the complex evolution of globular clusters (GCs). Recent studies have found that this cluster has interesting morphological features beyond the typical spherical distribution of GCs, suggesting that external tidal effects have played an important role in its evolution and current properties. Additionally, simulations have shown that NGC 5053 could be a likely candidate to belong to the Sagittarius dwarf galaxy (Sgr dSph) stream. Using the Wisconsin–Indiana–Yale–NOAO–Hydra multi-object spectrograph, we have collected high quality (signal-to-noise ratio ∼ 75–90), medium-resolution spectra for red giant branch stars in NGC 5053. Using these spectra we have measured the Fe, Ca, Ti, Ni, Ba, Na, and O abundances in the cluster. We measure an average cluster [Fe/H] abundance of −2.45 with a standard deviation of 0.04 dex, making NGC 5053 one of the most metal-poor GCs in the Milky Way (MW). The [Ca/Fe], [Ti/Fe], and [Ba/Fe] we measure are consistent with the abundances of MW halo stars at a similar metallicity, with alpha-enhanced ratios and slightly depleted [Ba/Fe]. The Na and O abundances show the Na–O anti-correlation found in most GCs. From our abundance analysis it appears that NGC 5053 is at least chemically similar to other GCs found in the MW. This does not, however, rule out NGC 5053 being associated with the Sgr dSph stream.

Chemical Abundances in NGC 5053: A Very Metal-poor and Dynamically Complex Globular Cluster

Science.gov (United States)

Boberg, Owen M.; Friel, Eileen D.; Vesperini, Enrico

2015-05-01

NGC 5053 provides a rich environment to test our understanding of the complex evolution of globular clusters (GCs). Recent studies have found that this cluster has interesting morphological features beyond the typical spherical distribution of GCs, suggesting that external tidal effects have played an important role in its evolution and current properties. Additionally, simulations have shown that NGC 5053 could be a likely candidate to belong to the Sagittarius dwarf galaxy (Sgr dSph) stream. Using the Wisconsin-Indiana-Yale-NOAO-Hydra multi-object spectrograph, we have collected high quality (signal-to-noise ratio ˜ 75-90), medium-resolution spectra for red giant branch stars in NGC 5053. Using these spectra we have measured the Fe, Ca, Ti, Ni, Ba, Na, and O abundances in the cluster. We measure an average cluster [Fe/H] abundance of -2.45 with a standard deviation of 0.04 dex, making NGC 5053 one of the most metal-poor GCs in the Milky Way (MW). The [Ca/Fe], [Ti/Fe], and [Ba/Fe] we measure are consistent with the abundances of MW halo stars at a similar metallicity, with alpha-enhanced ratios and slightly depleted [Ba/Fe]. The Na and O abundances show the Na-O anti-correlation found in most GCs. From our abundance analysis it appears that NGC 5053 is at least chemically similar to other GCs found in the MW. This does not, however, rule out NGC 5053 being associated with the Sgr dSph stream.

Chemical microreactor and method thereof

Science.gov (United States)

Morse, Jeffrey D [Martinez, CA; Jankowski, Alan [Livermore, CA

2011-08-09

A method for forming a chemical microreactor includes forming at least one capillary microchannel in a substrate having at least one inlet and at least one outlet, integrating at least one heater into the chemical microreactor, interfacing the capillary microchannel with a liquid chemical reservoir at the inlet of the capillary microchannel, and interfacing the capillary microchannel with a porous membrane near the outlet of the capillary microchannel, the porous membrane being positioned beyond the outlet of the capillary microchannel, wherein the porous membrane has at least one catalyst material imbedded therein.

Fragment-based {sup 13}C nuclear magnetic resonance chemical shift predictions in molecular crystals: An alternative to planewave methods

Energy Technology Data Exchange (ETDEWEB)

Hartman, Joshua D.; Beran, Gregory J. O., E-mail: gregory.beran@ucr.edu [Department of Chemistry, University of California, Riverside, California 92521 (United States); Monaco, Stephen; Schatschneider, Bohdan [The Pennsylvania State University, The Eberly Campus, 2201 University Dr, Lemont Furnace, Pennsylvania 15456 (United States)

2015-09-14

We assess the quality of fragment-based ab initio isotropic {sup 13}C chemical shift predictions for a collection of 25 molecular crystals with eight different density functionals. We explore the relative performance of cluster, two-body fragment, combined cluster/fragment, and the planewave gauge-including projector augmented wave (GIPAW) models relative to experiment. When electrostatic embedding is employed to capture many-body polarization effects, the simple and computationally inexpensive two-body fragment model predicts both isotropic {sup 13}C chemical shifts and the chemical shielding tensors as well as both cluster models and the GIPAW approach. Unlike the GIPAW approach, hybrid density functionals can be used readily in a fragment model, and all four hybrid functionals tested here (PBE0, B3LYP, B3PW91, and B97-2) predict chemical shifts in noticeably better agreement with experiment than the four generalized gradient approximation (GGA) functionals considered (PBE, OPBE, BLYP, and BP86). A set of recommended linear regression parameters for mapping between calculated chemical shieldings and observed chemical shifts are provided based on these benchmark calculations. Statistical cross-validation procedures are used to demonstrate the robustness of these fits.

a Three-Step Spatial-Temporal Clustering Method for Human Activity Pattern Analysis

Science.gov (United States)

Huang, W.; Li, S.; Xu, S.

2016-06-01

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people "say" for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the

Chemical inhomogeneity in InxGa1-xN and ZnO. A HRTEM study on atomic scale clustering

International Nuclear Information System (INIS)

Bartel, T.P.

2008-01-01

Nanostructuration as well as the nucleation and growth of nanoparticles pervades the development of modern materials and devices. Quantitative high resolution transmission electron microscopy (HRTEM) is currently being developed for a structural and chemical analysis at an atomic scale. It is used in this thesis to study the chemical inhomogeneity and clustering in In x Ga 1-x N, InN and ZnO. A methodology for reliable quantitative HRTEM is rst de ned: it necessitates a damage free sample, the avoidance of electron beam damage and the control of microscope instabilities. With these conditions satis ed, the reliability of quantitative HRTEM is demonstrated by an accurate measurement of lattice relaxation in a thin TEM sample. Clustering in an alloy can then be distinguished from a random distribution of atoms. In In x Ga 1-x N for instance, clustering is detected for concentrations x>0.1. The sensitivity is insufficient to determine whether clustering is present for lower concentrations. HRTEM allows to identify the amplitude and the spatial distribution of the decomposition which is attributed to a spinodal decomposition. In InN, nanometer scale metallic indium inclusions are detected. With decreasing size of the metallic clusters, the photoluminescence of the sample shifts towards the infrared. This indicates that the inclusions may be responsible for the infrared activity of InN. Finally, ZnO grown homoepitaxially on zinc-face and oxygen-face substrates is studied. The O-face epilayer is strained whereas the Zn-face epilayer is almost strain free and has a higher crystalline quality. Quantitative analysis of exit wave phases is in good agreement with simulations, but the signal to noise ratio needs to be improved for the detection of single point defects. (orig.)

Cluster size statistic and cluster mass statistic: two novel methods for identifying changes in functional connectivity between groups or conditions.

Science.gov (United States)

Ing, Alex; Schwarzbauer, Christian

2014-01-01

Functional connectivity has become an increasingly important area of research in recent years. At a typical spatial resolution, approximately 300 million connections link each voxel in the brain with every other. This pattern of connectivity is known as the functional connectome. Connectivity is often compared between experimental groups and conditions. Standard methods used to control the type 1 error rate are likely to be insensitive when comparisons are carried out across the whole connectome, due to the huge number of statistical tests involved. To address this problem, two new cluster based methods--the cluster size statistic (CSS) and cluster mass statistic (CMS)--are introduced to control the family wise error rate across all connectivity values. These methods operate within a statistical framework similar to the cluster based methods used in conventional task based fMRI. Both methods are data driven, permutation based and require minimal statistical assumptions. Here, the performance of each procedure is evaluated in a receiver operator characteristic (ROC) analysis, utilising a simulated dataset. The relative sensitivity of each method is also tested on real data: BOLD (blood oxygen level dependent) fMRI scans were carried out on twelve subjects under normal conditions and during the hypercapnic state (induced through the inhalation of 6% CO2 in 21% O2 and 73%N2). Both CSS and CMS detected significant changes in connectivity between normal and hypercapnic states. A family wise error correction carried out at the individual connection level exhibited no significant changes in connectivity.

Dynamic analysis of clustered building structures using substructures methods

International Nuclear Information System (INIS)

Leimbach, K.R.; Krutzik, N.J.

1989-01-01

The dynamic substructure approach to the building cluster on a common base mat starts with the generation of Ritz-vectors for each building on a rigid foundation. The base mat plus the foundation soil is subjected to kinematic constraint modes, for example constant, linear, quadratic or cubic constraints. These constraint modes are also imposed on the buildings. By enforcing kinematic compatibility of the complete structural system on the basis of the constraint modes a reduced Ritz model of the complete cluster is obtained. This reduced model can now be analyzed by modal time history or response spectrum methods

Comparison of cluster-based and source-attribution methods for estimating transmission risk using large HIV sequence databases.

Science.gov (United States)

Le Vu, Stéphane; Ratmann, Oliver; Delpech, Valerie; Brown, Alison E; Gill, O Noel; Tostevin, Anna; Fraser, Christophe; Volz, Erik M

2018-06-01

Phylogenetic clustering of HIV sequences from a random sample of patients can reveal epidemiological transmission patterns, but interpretation is hampered by limited theoretical support and statistical properties of clustering analysis remain poorly understood. Alternatively, source attribution methods allow fitting of HIV transmission models and thereby quantify aspects of disease transmission. A simulation study was conducted to assess error rates of clustering methods for detecting transmission risk factors. We modeled HIV epidemics among men having sex with men and generated phylogenies comparable to those that can be obtained from HIV surveillance data in the UK. Clustering and source attribution approaches were applied to evaluate their ability to identify patient attributes as transmission risk factors. We find that commonly used methods show a misleading association between cluster size or odds of clustering and covariates that are correlated with time since infection, regardless of their influence on transmission. Clustering methods usually have higher error rates and lower sensitivity than source attribution method for identifying transmission risk factors. But neither methods provide robust estimates of transmission risk ratios. Source attribution method can alleviate drawbacks from phylogenetic clustering but formal population genetic modeling may be required to estimate quantitative transmission risk factors. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Chemical control methods and tools

Science.gov (United States)

Steven Manning; James. Miller

2011-01-01

After determining the best course of action for control of an invasive plant population, it is important to understand the variety of methods available to the integrated pest management professional. A variety of methods are now widely used in managing invasive plants in natural areas, including chemical, mechanical, and cultural control methods. Once the preferred...

Innovative Strategies to Develop Chemical Categories Using a Combination of Structural and Toxicological Properties

Directory of Open Access Journals (Sweden)

Monika Batke

2016-09-01

Full Text Available 1.AbstractInterest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g. the European Union´s Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (<5. We discuss

Trend analysis using non-stationary time series clustering based on the finite element method

OpenAIRE

Gorji Sefidmazgi, M.; Sayemuzzaman, M.; Homaifar, A.; Jha, M. K.; Liess, S.

2014-01-01

In order to analyze low-frequency variability of climate, it is useful to model the climatic time series with multiple linear trends and locate the times of significant changes. In this paper, we have used non-stationary time series clustering to find change points in the trends. Clustering in a multi-dimensional non-stationary time series is challenging, since the problem is mathematically ill-posed. Clustering based on the finite element method (FEM) is one of the methods ...

Device for collecting chemical compounds and related methods

Science.gov (United States)

Scott, Jill R.; Groenewold, Gary S.; Rae, Catherine

2013-01-01

A device for sampling chemical compounds from fixed surfaces and related methods are disclosed. The device may include a vacuum source, a chamber and a sorbent material. The device may utilize vacuum extraction to volatilize the chemical compounds from the fixed surfaces so that they may be sorbed by the sorbent material. The sorbent material may then be analyzed using conventional thermal desorption/gas chromatography/mass spectrometry (TD/GC/MS) instrumentation to determine presence of the chemical compounds. The methods may include detecting release and presence of one or more chemical compounds and determining the efficacy of decontamination. The device may be useful in collection and analysis of a variety of chemical compounds, such as residual chemical warfare agents, chemical attribution signatures and toxic industrial chemicals.

Application of 2D and 3D image technologies to characterise morphological attributes of grapevine clusters.

Science.gov (United States)

Tello, Javier; Cubero, Sergio; Blasco, José; Tardaguila, Javier; Aleixos, Nuria; Ibáñez, Javier

2016-10-01

Grapevine cluster morphology influences the quality and commercial value of wine and table grapes. It is routinely evaluated by subjective and inaccurate methods that do not meet the requirements set by the food industry. Novel two-dimensional (2D) and three-dimensional (3D) machine vision technologies emerge as promising tools for its automatic and fast evaluation. The automatic evaluation of cluster length, width and elongation was successfully achieved by the analysis of 2D images, significant and strong correlations with the manual methods being found (r = 0.959, 0.861 and 0.852, respectively). The classification of clusters according to their shape can be achieved by evaluating their conicity in different sections of the cluster. The geometric reconstruction of the morphological volume of the cluster from 2D features worked better than the direct 3D laser scanning system, showing a high correlation (r = 0.956) with the manual approach (water displacement method). In addition, we constructed and validated a simple linear regression model for cluster compactness estimation. It showed a high predictive capacity for both the training and validation subsets of clusters (R(2)  = 84.5 and 71.1%, respectively). The methodologies proposed in this work provide continuous and accurate data for the fast and objective characterisation of cluster morphology. © 2016 Society of Chemical Industry. © 2016 Society of Chemical Industry.

Closed-cage tungsten oxide clusters in the gas phase.

Science.gov (United States)

Singh, D M David Jeba; Pradeep, T; Thirumoorthy, Krishnan; Balasubramanian, Krishnan

2010-05-06

During the course of a study on the clustering of W-Se and W-S mixtures in the gas phase using laser desorption ionization (LDI) mass spectrometry, we observed several anionic W-O clusters. Three distinct species, W(6)O(19)(-), W(13)O(29)(-), and W(14)O(32)(-), stand out as intense peaks in the regular mass spectral pattern of tungsten oxide clusters suggesting unusual stabilities for them. Moreover, these clusters do not fragment in the postsource decay analysis. While trying to understand the precursor material, which produced these clusters, we found the presence of nanoscale forms of tungsten oxide. The structure and thermodynamic parameters of tungsten clusters have been explored using relativistic quantum chemical methods. Our computed results of atomization energy are consistent with the observed LDI mass spectra. The computational results suggest that the clusters observed have closed-cage structure. These distinct W(13) and W(14) clusters were observed for the first time in the gas phase.

Study of methods to increase cluster/dislocation loop densities in electrodes

Science.gov (United States)

Yang, Xiaoling; Miley, George H.

2009-03-01

Recent research has developed a technique for imbedding ultra-high density deuterium ``clusters'' (50 to 100 atoms per cluster) in various metals such as Palladium (Pd), Beryllium (Be) and Lithium (Li). It was found the thermally dehydrogenated PdHx retained the clusters and exhibited up to 12 percent lower resistance compared to the virginal Pd samplesootnotetextA. G. Lipson, et al. Phys. Solid State. 39 (1997) 1891. SQUID measurements showed that in Pd these condensed matter clusters approach metallic conditions, exhibiting superconducting propertiesootnotetextA. Lipson, et al. Phys. Rev. B 72, 212507 (2005ootnotetextA. G. Lipson, et al. Phys. Lett. A 339, (2005) 414-423. If the fabrication methods under study are successful, a large packing fraction of nuclear reactive clusters can be developed in the electrodes by electrolyte or high pressure gas loading. This will provide a much higher low-energy-nuclear- reaction (LENR) rate than achieved with earlier electrodeootnotetextCastano, C.H., et al. Proc. ICCF-9, Beijing, China 19-24 May, 2002..

X-ray spectra of clusters of galaxies

Science.gov (United States)

Sarazin, Craig L.

1990-01-01

X-ray line observations of clusters of galaxies have shown that the X-ray emission in clusters is mainly thermal emission from hot diffuse gas, and that much of this gas has come out of stars, probably having been ejected from galaxies in the cluster. Future high resolution observations should allow us to determine the physical state of the gas. X-ray line measurements and abundance determinations can lead to strong constraints on the origin of the intracluster gas, and on the chemical evolution and history of galaxies. Some of the stronger resonant X-ray lines may be observable as absorption lines against a background quasar. Such X-ray absorption line measurement can be used to directly derive distances to clusters, using a technique similar to (and possibly complementary to) that the well-known method using the Zel'dovich-Syunyaev effect.

A Chemical Composition Survey of the Iron-complex Globular Cluster NGC 6273 (M19)

Energy Technology Data Exchange (ETDEWEB)

Johnson, Christian I.; Caldwell, Nelson [Harvard–Smithsonian Center for Astrophysics, 60 Garden Street, MS-15, Cambridge, MA 02138 (United States); Rich, R. Michael [Department of Physics and Astronomy, UCLA, 430 Portola Plaza, Box 951547, Los Angeles, CA 90095-1547 (United States); Mateo, Mario [Department of Astronomy, University of Michigan, Ann Arbor, MI 48109 (United States); Bailey, John I. III [Leiden Observatory, Leiden University, P.O. Box 9513, 2300RA Leiden (Netherlands); Clarkson, William I. [Department of Natural Sciences, University of Michigan–Dearborn, 4901 Evergreen Road, Dearborn, MI 48128 (United States); Olszewski, Edward W. [Steward Observatory, The University of Arizona, 933 N. Cherry Avenue, Tucson, AZ 85721 (United States); Walker, Matthew G., E-mail: cjohnson@cfa.harvard.edu, E-mail: ncaldwell@cfa.harvard.edu, E-mail: rmr@astro.ucla.edu, E-mail: mmateo@umich.edu, E-mail: baileyji@strw.leidenuniv.nl, E-mail: wiclarks@umich.edu, E-mail: eolszewski@as.arizona.edu, E-mail: mgwalker@andrew.cmu.edu [McWilliams Center for Cosmology, Department of Physics, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213 (United States)

2017-02-20

Recent observations have shown that a growing number of the most massive Galactic globular clusters contain multiple populations of stars with different [Fe/H] and neutron-capture element abundances. NGC 6273 has only recently been recognized as a member of this “iron-complex” cluster class, and we provide here a chemical and kinematic analysis of >300 red giant branch and asymptotic giant branch member stars using high-resolution spectra obtained with the Magellan –M2FS and VLT–FLAMES instruments. Multiple lines of evidence indicate that NGC 6273 possesses an intrinsic metallicity spread that ranges from about [Fe/H] = −2 to −1 dex, and may include at least three populations with different [Fe/H] values. The three populations identified here contain separate first (Na/Al-poor) and second (Na/Al-rich) generation stars, but a Mg–Al anti-correlation may only be present in stars with [Fe/H] ≳ −1.65. The strong correlation between [La/Eu] and [Fe/H] suggests that the s-process must have dominated the heavy element enrichment at higher metallicities. A small group of stars with low [ α /Fe] is identified and may have been accreted from a former surrounding field star population. The cluster’s large abundance variations are coupled with a complex, extended, and multimodal blue horizontal branch (HB). The HB morphology and chemical abundances suggest that NGC 6273 may have an origin that is similar to ω Cen and M54.

Chemical Abundance Evidence of Enduring High Star Formation Rates in an Early-type Galaxy: High [Ca/Fe] in NGC 5128 Globular Clusters

Science.gov (United States)

Colucci, Janet E.; Fernanda Durán, María; Bernstein, Rebecca A.; McWilliam, Andrew

2013-08-01

We present [Fe/H], ages, and Ca abundances for an initial sample of 10 globular clusters in NGC 5128 obtained from high-resolution, high signal-to-noise ratio echelle spectra of their integrated light. All abundances and ages are obtained using our original technique for high-resolution integrated light abundance analysis of globular clusters. The clusters have a range in [Fe/H] between -1.6 and -0.2. In this sample, the average [Ca/Fe] for clusters with [Fe/H] <-0.4 is +0.37 ± 0.07, while the average [Ca/Fe] in our Milky Way (MW) and M31 GC samples is +0.29 ± 0.09 and +0.24 ± 0.10, respectively. This may imply a more rapid chemical enrichment history for NGC 5128 than for either the MW or M31. This sample provides the first quantitative picture of the chemical history of NGC 5128 that is directly comparable to what is available for the MW. Data presented here were obtained with the MIKE echelle spectrograph on the Magellan Clay Telescope. This Letter includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

Contribution of radiation chemistry to cluster science

International Nuclear Information System (INIS)

Belloni, J.

2006-01-01

Nanoclusters are small objects made of a few atoms, with a size of a few nanometers at most, which constitute a state of matter, named mesoscopic, intermediary between the atom and the bulk metal. In the 70's, radiation chemistry experiments have demonstrated that metal clusters exhibited indeed, due to their very small size, specific properties distinct from the bulk metal. The properties, physical and chemical, change with the number of atoms they contain. Their optical absorption spectrum, for example, as well as their redox potential, depends on the nuclearity, and also on the environment. Radiation chemistry methods have been proven to be of high potentiality to induce small and size-monodispersed metal clusters, as nanocolloids or supported on various materials. Pulse radiolysis provides the means to study the dynamics of nucleation and growth of clusters, monoand bi-metallic, from the monomers to the stable nanoparticle and to observe directly their reactivity, especially to determine during the growth their nuclearity-dependent properties, such as the redox potential. These are of crucial importance for the understanding of the mechanism of the cluster growth itself, in the radiation-induced as well as in the chemical or photochemical reduction processes, and also of the mechanism of certain catalytic reactions. (authors)

Don't spin the pen: two alternative methods for second-stage sampling in urban cluster surveys

Directory of Open Access Journals (Sweden)

Rose Angela MC

2007-06-01

Full Text Available Abstract In two-stage cluster surveys, the traditional method used in second-stage sampling (in which the first household in a cluster is selected is time-consuming and may result in biased estimates of the indicator of interest. Firstly, a random direction from the center of the cluster is selected, usually by spinning a pen. The houses along that direction are then counted out to the boundary of the cluster, and one is then selected at random to be the first household surveyed. This process favors households towards the center of the cluster, but it could easily be improved. During a recent meningitis vaccination coverage survey in Maradi, Niger, we compared this method of first household selection to two alternatives in urban zones: 1 using a superimposed grid on the map of the cluster area and randomly selecting an intersection; and 2 drawing the perimeter of the cluster area using a Global Positioning System (GPS and randomly selecting one point within the perimeter. Although we only compared a limited number of clusters using each method, we found the sampling grid method to be the fastest and easiest for field survey teams, although it does require a map of the area. Selecting a random GPS point was also found to be a good method, once adequate training can be provided. Spinning the pen and counting households to the boundary was the most complicated and time-consuming. The two methods tested here represent simpler, quicker and potentially more robust alternatives to spinning the pen for cluster surveys in urban areas. However, in rural areas, these alternatives would favor initial household selection from lower density (or even potentially empty areas. Bearing in mind these limitations, as well as available resources and feasibility, investigators should choose the most appropriate method for their particular survey context.

Solvation effects on chemical shifts by embedded cluster integral equation theory.

Science.gov (United States)

Frach, Roland; Kast, Stefan M

2014-12-11

The accurate computational prediction of nuclear magnetic resonance (NMR) parameters like chemical shifts represents a challenge if the species studied is immersed in strongly polarizing environments such as water. Common approaches to treating a solvent in the form of, e.g., the polarizable continuum model (PCM) ignore strong directional interactions such as H-bonds to the solvent which can have substantial impact on magnetic shieldings. We here present a computational methodology that accounts for atomic-level solvent effects on NMR parameters by extending the embedded cluster reference interaction site model (EC-RISM) integral equation theory to the prediction of chemical shifts of N-methylacetamide (NMA) in aqueous solution. We examine the influence of various so-called closure approximations of the underlying three-dimensional RISM theory as well as the impact of basis set size and different treatment of electrostatic solute-solvent interactions. We find considerable and systematic improvement over reference PCM and gas phase calculations. A smaller basis set in combination with a simple point charge model already yields good performance which can be further improved by employing exact electrostatic quantum-mechanical solute-solvent interaction energies. A larger basis set benefits more significantly from exact over point charge electrostatics, which can be related to differences of the solvent's charge distribution.

A Multidimensional and Multimembership Clustering Method for Social Networks and Its Application in Customer Relationship Management

Directory of Open Access Journals (Sweden)

Peixin Zhao

2013-01-01

Full Text Available Community detection in social networks plays an important role in cluster analysis. Many traditional techniques for one-dimensional problems have been proven inadequate for high-dimensional or mixed type datasets due to the data sparseness and attribute redundancy. In this paper we propose a graph-based clustering method for multidimensional datasets. This novel method has two distinguished features: nonbinary hierarchical tree and the multi-membership clusters. The nonbinary hierarchical tree clearly highlights meaningful clusters, while the multimembership feature may provide more useful service strategies. Experimental results on the customer relationship management confirm the effectiveness of the new method.

Diversity among galaxy clusters

International Nuclear Information System (INIS)

Struble, M.F.; Rood, H.J.

1988-01-01

The classification of galaxy clusters is discussed. Consideration is given to the classification scheme of Abell (1950's), Zwicky (1950's), Morgan, Matthews, and Schmidt (1964), and Morgan-Bautz (1970). Galaxies can be classified based on morphology, chemical composition, spatial distribution, and motion. The correlation between a galaxy's environment and morphology is examined. The classification scheme of Rood-Sastry (1971), which is based on clusters's morphology and galaxy population, is described. The six types of clusters they define include: (1) a cD-cluster dominated by a single large galaxy, (2) a cluster dominated by a binary, (3) a core-halo cluster, (4) a cluster dominated by several bright galaxies, (5) a cluster appearing flattened, and (6) an irregularly shaped cluster. Attention is also given to the evolution of cluster structures, which is related to initial density and cluster motion

LMC clusters: young

International Nuclear Information System (INIS)

Freeman, K.C.

1980-01-01

The young globular clusters of the LMC have ages of 10 7 -10 8 y. Their masses and structure are similar to those of the smaller galactic globular clusters. Their stellar mass functions (in the mass range 6 solar masses to 1.2 solar masses) vary greatly from cluster to cluster, although the clusters are similar in total mass, age, structure and chemical composition. It would be very interesting to know why these clusters are forming now in the LMC and not in the Galaxy. The author considers the 'young globular' or 'blue populous' clusters of the LMC. The ages of these objects are 10 7 to 10 8 y, and their masses are 10 4 to 10 5 solar masses, so they are populous enough to be really useful for studying the evolution of massive stars. The author concentrates on the structure and stellar content of these young clusters. (Auth.)

Cobalt(III)-oxo cubane clusters as catalysts for oxidation of organic ...

Indian Academy of Sciences (India)

been prepared by a general method and these have been characterized by analytical, spectroscopic, electro- chemical and ... alkylaromatics, alkenes and alcohols.1 Several indus- .... us to obtain the cobalt(III)-oxo clusters in good to very.

From quantum chemical formation free energies to evaporation rates

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I. K. Ortega

2012-01-01

Full Text Available Atmospheric new particle formation is an important source of atmospheric aerosols. Large efforts have been made during the past few years to identify which molecules are behind this phenomenon, but the actual birth mechanism of the particles is not yet well known. Quantum chemical calculations have proven to be a powerful tool to gain new insights into the very first steps of particle formation. In the present study we use formation free energies calculated by quantum chemical methods to estimate the evaporation rates of species from sulfuric acid clusters containing ammonia or dimethylamine. We have found that dimethylamine forms much more stable clusters with sulphuric acid than ammonia does. On the other hand, the existence of a very deep local minimum for clusters with two sulfuric acid molecules and two dimethylamine molecules hinders their growth to larger clusters. These results indicate that other compounds may be needed to make clusters grow to larger sizes (containing more than three sulfuric acid molecules.

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

Science.gov (United States)

Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

2015-01-01

The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

Heuristic methods using grasp, path relinking and variable neighborhood search for the clustered traveling salesman problem

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Mário Mestria

2013-08-01

Full Text Available The Clustered Traveling Salesman Problem (CTSP is a generalization of the Traveling Salesman Problem (TSP in which the set of vertices is partitioned into disjoint clusters and objective is to find a minimum cost Hamiltonian cycle such that the vertices of each cluster are visited contiguously. The CTSP is NP-hard and, in this context, we are proposed heuristic methods for the CTSP using GRASP, Path Relinking and Variable Neighborhood Descent (VND. The heuristic methods were tested using Euclidean instances with up to 2000 vertices and clusters varying between 4 to 150 vertices. The computational tests were performed to compare the performance of the heuristic methods with an exact algorithm using the Parallel CPLEX software. The computational results showed that the hybrid heuristic method using VND outperforms other heuristic methods.

A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS

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W. Huang

2016-06-01

Full Text Available How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis. In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time to four dimensions (space, time and semantics. More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them. Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The

PARTIAL TRAINING METHOD FOR HEURISTIC ALGORITHM OF POSSIBLE CLUSTERIZATION UNDER UNKNOWN NUMBER OF CLASSES

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D. A. Viattchenin

2009-01-01

Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible  clusterization with partial  training is proposed in the  paper.  The  method  is  based  on  data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.

De novo clustering methods outperform reference-based methods for assigning 16S rRNA gene sequences to operational taxonomic units

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Sarah L. Westcott

2015-12-01

Full Text Available Background. 16S rRNA gene sequences are routinely assigned to operational taxonomic units (OTUs that are then used to analyze complex microbial communities. A number of methods have been employed to carry out the assignment of 16S rRNA gene sequences to OTUs leading to confusion over which method is optimal. A recent study suggested that a clustering method should be selected based on its ability to generate stable OTU assignments that do not change as additional sequences are added to the dataset. In contrast, we contend that the quality of the OTU assignments, the ability of the method to properly represent the distances between the sequences, is more important.Methods. Our analysis implemented six de novo clustering algorithms including the single linkage, complete linkage, average linkage, abundance-based greedy clustering, distance-based greedy clustering, and Swarm and the open and closed-reference methods. Using two previously published datasets we used the Matthew’s Correlation Coefficient (MCC to assess the stability and quality of OTU assignments.Results. The stability of OTU assignments did not reflect the quality of the assignments. Depending on the dataset being analyzed, the average linkage and the distance and abundance-based greedy clustering methods generated OTUs that were more likely to represent the actual distances between sequences than the open and closed-reference methods. We also demonstrated that for the greedy algorithms VSEARCH produced assignments that were comparable to those produced by USEARCH making VSEARCH a viable free and open source alternative to USEARCH. Further interrogation of the reference-based methods indicated that when USEARCH or VSEARCH were used to identify the closest reference, the OTU assignments were sensitive to the order of the reference sequences because the reference sequences can be identical over the region being considered. More troubling was the observation that while both USEARCH and

Statistical method on nonrandom clustering with application to somatic mutations in cancer

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Rejto Paul A

2010-01-01

Full Text Available Abstract Background Human cancer is caused by the accumulation of tumor-specific mutations in oncogenes and tumor suppressors that confer a selective growth advantage to cells. As a consequence of genomic instability and high levels of proliferation, many passenger mutations that do not contribute to the cancer phenotype arise alongside mutations that drive oncogenesis. While several approaches have been developed to separate driver mutations from passengers, few approaches can specifically identify activating driver mutations in oncogenes, which are more amenable for pharmacological intervention. Results We propose a new statistical method for detecting activating mutations in cancer by identifying nonrandom clusters of amino acid mutations in protein sequences. A probability model is derived using order statistics assuming that the location of amino acid mutations on a protein follows a uniform distribution. Our statistical measure is the differences between pair-wise order statistics, which is equivalent to the size of an amino acid mutation cluster, and the probabilities are derived from exact and approximate distributions of the statistical measure. Using data in the Catalog of Somatic Mutations in Cancer (COSMIC database, we have demonstrated that our method detects well-known clusters of activating mutations in KRAS, BRAF, PI3K, and β-catenin. The method can also identify new cancer targets as well as gain-of-function mutations in tumor suppressors. Conclusions Our proposed method is useful to discover activating driver mutations in cancer by identifying nonrandom clusters of somatic amino acid mutations in protein sequences.

Discrimination and chemical phylogenetic study of seven species of Dendrobium using infrared spectroscopy combined with cluster analysis

Science.gov (United States)

Luo, Congpei; He, Tao; Chun, Ze

2013-04-01

Dendrobium is a commonly used and precious herb in Traditional Chinese Medicine. The high biodiversity of Dendrobium and the therapeutic needs require tools for the correct and fast discrimination of different Dendrobium species. This study investigates Fourier transform infrared spectroscopy followed by cluster analysis for discrimination and chemical phylogenetic study of seven Dendrobium species. Despite the general pattern of the IR spectra, different intensities, shapes, peak positions were found in the IR spectra of these samples, especially in the range of 1800-800 cm-1. The second derivative transformation and alcoholic extracting procedure obviously enlarged the tiny spectral differences among these samples. The results indicated each Dendrobium species had a characteristic IR spectra profile, which could be used to discriminate them. The similarity coefficients among the samples were analyzed based on their second derivative IR spectra, which ranged from 0.7632 to 0.9700, among the seven Dendrobium species, and from 0.5163 to 0.9615, among the ethanol extracts. A dendrogram was constructed based on cluster analysis the IR spectra for studying the chemical phylogenetic relationships among the samples. The results indicated that D. denneanum and D. crepidatum could be the alternative resources to substitute D. chrysotoxum, D. officinale and D. nobile which were officially recorded in Chinese Pharmacopoeia. In conclusion, with the advantages of high resolution, speediness and convenience, the experimental approach can successfully discriminate and construct the chemical phylogenetic relationships of the seven Dendrobium species.

A clustering based method to evaluate soil corrosivity for pipeline external integrity management

International Nuclear Information System (INIS)

Yajima, Ayako; Wang, Hui; Liang, Robert Y.; Castaneda, Homero

2015-01-01

One important category of transportation infrastructure is underground pipelines. Corrosion of these buried pipeline systems may cause pipeline failures with the attendant hazards of property loss and fatalities. Therefore, developing the capability to estimate the soil corrosivity is important for designing and preserving materials and for risk assessment. The deterioration rate of metal is highly influenced by the physicochemical characteristics of a material and the environment of its surroundings. In this study, the field data obtained from the southeast region of Mexico was examined using various data mining techniques to determine the usefulness of these techniques for clustering soil corrosivity level. Specifically, the soil was classified into different corrosivity level clusters by k-means and Gaussian mixture model (GMM). In terms of physical space, GMM shows better separability; therefore, the distributions of the material loss of the buried petroleum pipeline walls were estimated via the empirical density within GMM clusters. The soil corrosivity levels of the clusters were determined based on the medians of metal loss. The proposed clustering method was demonstrated to be capable of classifying the soil into different levels of corrosivity severity. - Highlights: • The clustering approach is applied to the data extracted from a real-life pipeline system. • Soil properties in the right-of-way are analyzed via clustering techniques to assess corrosivity. • GMM is selected as the preferred method for detecting the hidden pattern of in-situ data. • K–W test is performed for significant difference of corrosivity level between clusters

First-principle study of silicon cluster doped with rhodium: Rh{sub 2}Si{sub n} (n = 1–11) clusters

Energy Technology Data Exchange (ETDEWEB)

Zhang, Shuai; Luo, Chang Geng; Li, Hua Yang [Department of Physics, Nanyang Normal University, Nanyang 473061 (China); Lu, Cheng, E-mail: lucheng@calypso.cn [Department of Physics, Nanyang Normal University, Nanyang 473061 (China); State Key Laboratory of Superhard Materials, Jilin University, Changchun 130012 (China); Li, Gen Quan; Lu, Zhi Wen [Department of Physics, Nanyang Normal University, Nanyang 473061 (China)

2015-06-15

The geometries, stabilities and electronic properties of rhodium-doped silicon clusters Rh{sub 2}Si{sub n} (n = 1–11) have been systematically studied by using density functional calculations at the B3LYP/GENECP level. The optimized results show that the lowest-energy isomers of Rh{sub 2}Si{sub n} clusters favor three-dimensional structures for n = 2–11. Based on the averaged binding energy, fragmentation energy, second-order energy difference and HOMO-LUMO energy gap, the stabilities of Rh{sub 2}Si{sub n} (n = 1–11) clusters have been analyzed. The calculated results suggest that the Rh{sub 2}Si{sub 6} cluster has the strongest relative stability and the doping with rhodium atoms can reduce the chemical stabilities of Si{sub n} clusters. The natural population and natural electron configuration analysis indicate that there is charge transfer from the Si atoms and 5s orbital of the Rh atoms to the 4d and 5p orbitals of Rh atoms. The analysis of electron localization function reveal that the Si–Si bonds are mainly covalent bonds and the Si–Rh bonds are almost ionic bonds. Moreover, the vertical ionization potential, vertical electron affinity, chemical hardness, chemical potential, vibrational spectrum and polarizability are also discussed. - Highlights: • The geometric structures of Rh{sub 2}Si{sub n} (n = 1–11) clusters are determined. • The stabilities and electronic properties of Rh{sub 2}Si{sub n} clusters are discussed. • The Rh{sub 2}Si{sub 6} cluster has the higher stability than other clusters. • The doped rhodium atoms can reduce the chemical stabilities of Si{sub n} clusters.

Chemical abundances of giant stars in NGC 5053 and NGC 5634, two globular clusters associated with the Sagittarius dwarf spheroidal galaxy?

Science.gov (United States)

Sbordone, L.; Monaco, L.; Moni Bidin, C.; Bonifacio, P.; Villanova, S.; Bellazzini, M.; Ibata, R.; Chiba, M.; Geisler, D.; Caffau, E.; Duffau, S.

2015-07-01

Context. The tidal disruption of the Sagittarius dwarf spheroidal galaxy (Sgr dSph) is producing the most prominent substructure in the Milky Way (MW) halo, the Sagittarius Stream. Aside from field stars, it is suspected that the Sgr dSph has lost a number of globular clusters (GC). Many Galactic GC are thought to have originated in the Sgr dSph. While for some candidates an origin in the Sgr dSph has been confirmed owing to chemical similarities, others exist whose chemical composition has never been investigated. Aims: NGC 5053 and NGC 5634 are two of these scarcely studied Sgr dSph candidate-member clusters. To characterize their composition we analyzed one giant star in NGC 5053, and two in NGC 5634. Methods: We analyze high-resolution and signal-to-noise spectra by means of the MyGIsFOS code, determining atmospheric parameters and abundances for up to 21 species between O and Eu. The abundances are compared with those of MW halo field stars, of unassociated MW halo globulars, and of the metal-poor Sgr dSph main body population. Results: We derive a metallicity of [Fe ii/H] = -2.26 ± 0.10 for NGC 5053, and of [Fe i/H] = -1.99 ± 0.075 and -1.97 ± 0.076 for the two stars in NGC 5634. This makes NGC 5053 one of the most metal-poor globular clusters in the MW. Both clusters display an α enhancement similar to the one of the halo at comparable metallicity. The two stars in NGC 5634 clearly display the Na-O anticorrelation widespread among MW globulars. Most other abundances are in good agreement with standard MW halo trends. Conclusions: The chemistry of the Sgr dSph main body populations is similar to that of the halo at low metallicity. It is thus difficult to discriminate between an origin of NGC 5053 and NGC 5634 in the Sgr dSph, and one in the MW. However, the abundances of these clusters do appear closer to that of Sgr dSph than of the halo, favoring an origin in the Sgr dSph system. Appendix A is available in electronic form at http

The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB.

Science.gov (United States)

Sander, Ulrich; Lubbe, Nils

2018-04-01

Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The

Investigation of Evaluation method of chemical runaway reaction

International Nuclear Information System (INIS)

Sato, Yoshihiko; Sasaya, Shinji; Kurakata, Koichiro; Nojiri, Ichiro

2002-02-01

Safety study 'Study of evaluation of abnormal occurrence for chemical substances in the nuclear fuel facilities' will be carried out from 2001 to 2005. In this study, the prediction of thermal hazards of chemical substances will be investigated and prepared. The hazard prediction method of chemical substances will be constructed from these results. Therefore, the hazard prediction methods applied in the chemical engineering in which the chemical substances with the hazard of fire and explosion were often treated were investigated. CHETAH (The ASTM Computer Program for Chemical Thermodynamic and Energy Release Evaluation) developed by ASTM (American Society for Testing and Materials) and TSS (Thermal Safety Software) developed by CISP (ChemInform St. Petersburg) were introduced and the fire and explosion hazards of chemical substances and reactions in the reprocessing process were evaluated. From these evaluated results, CHETAH could almost estimate the heat of reaction at 10% accuracy. It was supposed that CHETAH was useful as a screening for the hazards of fire and explosion of the new chemical substances and so on. TSS could calculate the reaction rate and the reaction behavior from the data measured by the various calorimeters rapidly. It was supposed that TSS was useful as an evaluation method for the hazards of fire and explosion of the new chemical reactions and so on. (author)

Unsupervised Learning —A Novel Clustering Method for Rolling Bearing Faults Identification

Science.gov (United States)

Kai, Li; Bo, Luo; Tao, Ma; Xuefeng, Yang; Guangming, Wang

2017-12-01

To promptly process the massive fault data and automatically provide accurate diagnosis results, numerous studies have been conducted on intelligent fault diagnosis of rolling bearing. Among these studies, such as artificial neural networks, support vector machines, decision trees and other supervised learning methods are used commonly. These methods can detect the failure of rolling bearing effectively, but to achieve better detection results, it often requires a lot of training samples. Based on above, a novel clustering method is proposed in this paper. This novel method is able to find the correct number of clusters automatically the effectiveness of the proposed method is validated using datasets from rolling element bearings. The diagnosis results show that the proposed method can accurately detect the fault types of small samples. Meanwhile, the diagnosis results are also relative high accuracy even for massive samples.

Investigation of the cluster formation in lithium niobate crystals by computer modeling method

Energy Technology Data Exchange (ETDEWEB)

Voskresenskii, V. M.; Starodub, O. R., E-mail: ol-star@mail.ru; Sidorov, N. V.; Palatnikov, M. N. [Russian Academy of Sciences, Tananaev Institute of Chemistry and Technology of Rare Earth Elements and Mineral Raw Materials, Kola Science Centre (Russian Federation)

2017-03-15

The processes occurring upon the formation of energetically equilibrium oxygen-octahedral clusters in the ferroelectric phase of a stoichiometric lithium niobate (LiNbO{sub 3}) crystal have been investigated by the computer modeling method within the semiclassical atomistic model. An energetically favorable cluster size (at which a structure similar to that of a congruent crystal is organized) is shown to exist. A stoichiometric cluster cannot exist because of the electroneutrality loss. The most energetically favorable cluster is that with a Li/Nb ratio of about 0.945, a value close to the lithium-to-niobium ratio for a congruent crystal.

Threshold selection for classification of MR brain images by clustering method

Energy Technology Data Exchange (ETDEWEB)

Moldovanu, Simona [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania); Dumitru Moţoc High School, 15 Milcov St., 800509, Galaţi (Romania); Obreja, Cristian; Moraru, Luminita, E-mail: luminita.moraru@ugal.ro [Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunărea de Jos University of Galaţi, 47 Domnească St., 800008, Romania, Phone: +40 236 460 780 (Romania)

2015-12-07

Given a grey-intensity image, our method detects the optimal threshold for a suitable binarization of MR brain images. In MR brain image processing, the grey levels of pixels belonging to the object are not substantially different from the grey levels belonging to the background. Threshold optimization is an effective tool to separate objects from the background and further, in classification applications. This paper gives a detailed investigation on the selection of thresholds. Our method does not use the well-known method for binarization. Instead, we perform a simple threshold optimization which, in turn, will allow the best classification of the analyzed images into healthy and multiple sclerosis disease. The dissimilarity (or the distance between classes) has been established using the clustering method based on dendrograms. We tested our method using two classes of images: the first consists of 20 T2-weighted and 20 proton density PD-weighted scans from two healthy subjects and from two patients with multiple sclerosis. For each image and for each threshold, the number of the white pixels (or the area of white objects in binary image) has been determined. These pixel numbers represent the objects in clustering operation. The following optimum threshold values are obtained, T = 80 for PD images and T = 30 for T2w images. Each mentioned threshold separate clearly the clusters that belonging of the studied groups, healthy patient and multiple sclerosis disease.

Grey Wolf Optimizer Based on Powell Local Optimization Method for Clustering Analysis

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Sen Zhang

2015-01-01

Full Text Available One heuristic evolutionary algorithm recently proposed is the grey wolf optimizer (GWO, inspired by the leadership hierarchy and hunting mechanism of grey wolves in nature. This paper presents an extended GWO algorithm based on Powell local optimization method, and we call it PGWO. PGWO algorithm significantly improves the original GWO in solving complex optimization problems. Clustering is a popular data analysis and data mining technique. Hence, the PGWO could be applied in solving clustering problems. In this study, first the PGWO algorithm is tested on seven benchmark functions. Second, the PGWO algorithm is used for data clustering on nine data sets. Compared to other state-of-the-art evolutionary algorithms, the results of benchmark and data clustering demonstrate the superior performance of PGWO algorithm.

Application Of WIMS Code To Calculation Kartini Reactor Parameters By Pin-Cell And Cluster Method

International Nuclear Information System (INIS)

Sumarsono, Bambang; Tjiptono, T.W.

1996-01-01

Analysis UZrH fuel element parameters calculation in Kartini Reactor by WIMS Code has been done. The analysis is done by pin cell and cluster method. The pin cell method is done as a function percent burn-up and by 8 group 3 region analysis and cluster method by 8 group 12 region analysis. From analysis and calculation resulted K ∼ = 1.3687 by pin cell method and K ∼ = 1.3162 by cluster method and so deviation is 3.83%. By pin cell analysis as a function percent burn-up at the percent burn-up greater than 59.50%, the multiplication factor is less than one (k ∼ < 1) it is mean that the fuel element reactivity is negative

Variational cluster perturbation theory for Bose-Hubbard models

International Nuclear Information System (INIS)

Koller, W; Dupuis, N

2006-01-01

We discuss the application of the variational cluster perturbation theory (VCPT) to the Mott-insulator-to-superfluid transition in the Bose-Hubbard model. We show how the VCPT can be formulated in such a way that it gives a translation invariant excitation spectrum-free of spurious gaps-despite the fact that it formally breaks translation invariance. The phase diagram and the single-particle Green function in the insulating phase are obtained for one-dimensional systems. When the chemical potential of the cluster is taken as a variational parameter, the VCPT reproduces the dimensional dependence of the phase diagram even for one-site clusters. We find a good quantitative agreement with the results of the density-matrix renormalization group when the number of sites in the cluster becomes of order 10. The extension of the method to the superfluid phase is discussed

Puzzle of magnetic moments of Ni clusters revisited using quantum Monte Carlo method.

Science.gov (United States)

Lee, Hung-Wen; Chang, Chun-Ming; Hsing, Cheng-Rong

2017-02-28

The puzzle of the magnetic moments of small nickel clusters arises from the discrepancy between values predicted using density functional theory (DFT) and experimental measurements. Traditional DFT approaches underestimate the magnetic moments of nickel clusters. Two fundamental problems are associated with this puzzle, namely, calculating the exchange-correlation interaction accurately and determining the global minimum structures of the clusters. Theoretically, the two problems can be solved using quantum Monte Carlo (QMC) calculations and the ab initio random structure searching (AIRSS) method correspondingly. Therefore, we combined the fixed-moment AIRSS and QMC methods to investigate the magnetic properties of Ni n (n = 5-9) clusters. The spin moments of the diffusion Monte Carlo (DMC) ground states are higher than those of the Perdew-Burke-Ernzerhof ground states and, in the case of Ni 8-9 , two new ground-state structures have been discovered using the DMC calculations. The predicted results are closer to the experimental findings, unlike the results predicted in previous standard DFT studies.

Chemical Methods to Knock Down the Amyloid Proteins

Directory of Open Access Journals (Sweden)

Na Gao

2017-06-01

Full Text Available Amyloid proteins are closely related with amyloid diseases and do tremendous harm to human health. However, there is still a lack of effective strategies to treat these amyloid diseases, so it is important to develop novel methods. Accelerating the clearance of amyloid proteins is a favorable method for amyloid disease treatment. Recently, chemical methods for protein reduction have been developed and have attracted much attention. In this review, we focus on the latest progress of chemical methods that knock down amyloid proteins, including the proteolysis-targeting chimera (PROTAC strategy, the “recognition-cleavage” strategy, the chaperone-mediated autophagy (CMA strategy, the selectively light-activatable organic and inorganic molecules strategy and other chemical strategies.

Analysis of cost data in a cluster-randomized, controlled trial: comparison of methods

DEFF Research Database (Denmark)

Sokolowski, Ineta; Ørnbøl, Eva; Rosendal, Marianne

studies have used non-valid analysis of skewed data. We propose two different methods to compare mean cost in two groups. Firstly, we use a non-parametric bootstrap method where the re-sampling takes place on two levels in order to take into account the cluster effect. Secondly, we proceed with a log......-transformation of the cost data and apply the normal theory on these data. Again we try to account for the cluster effect. The performance of these two methods is investigated in a simulation study. The advantages and disadvantages of the different approaches are discussed.......  We consider health care data from a cluster-randomized intervention study in primary care to test whether the average health care costs among study patients differ between the two groups. The problems of analysing cost data are that most data are severely skewed. Median instead of mean...

Dynamic Fuzzy Clustering Method for Decision Support in Electricity Markets Negotiation

Directory of Open Access Journals (Sweden)

Ricardo FAIA

2016-10-01

Full Text Available Artificial Intelligence (AI methods contribute to the construction of systems where there is a need to automate the tasks. They are typically used for problems that have a large response time, or when a mathematical method cannot be used to solve the problem. However, the application of AI brings an added complexity to the development of such applications. AI has been frequently applied in the power systems field, namely in Electricity Markets (EM. In this area, AI applications are essentially used to forecast / estimate the prices of electricity or to search for the best opportunity to sell the product. This paper proposes a clustering methodology that is combined with fuzzy logic in order to perform the estimation of EM prices. The proposed method is based on the application of a clustering methodology that groups historic energy contracts according to their prices’ similarity. The optimal number of groups is automatically calculated taking into account the preference for the balance between the estimation error and the number of groups. The centroids of each cluster are used to define a dynamic fuzzy variable that approximates the tendency of contracts’ history. The resulting fuzzy variable allows estimating expected prices for contracts instantaneously and approximating missing values in the historic contracts.

An investigation on the structure, spectroscopy and thermodynamic aspects of Br2((-))(H2O)n clusters using a conjunction of stochastic and quantum chemical methods.

Science.gov (United States)

Naskar, Pulak; Chaudhury, Pinaki

2016-06-28

In this work we obtained global as well as local structures of Br2((-))(H2O)n clusters for n = 2 to 6 followed by the study of IR-spectral features and thermochemistry for the structures. The way adopted by us to obtain structures is not the conventional one used in most cases. Here we at first generated excellent quality pre-optimized structures by exploring the suitable empirical potential energy surface using stochastic optimizer simulated annealing. These structures are then further refined using quantum chemical calculations to obtain the final structures, and spectral and thermodynamic features. We clearly showed that our approach results in very quick and better convergence which reduces the computational cost and obviously using the strategy we are able to get one [i.e. global] or more than one [i.e. global and local(s)] energetically lower structures than those which are already reported for a given cluster size. Moreover, IR-spectral results and the evolutionary trends in interaction energy, solvation energy and vertical detachment energy for global structures of each size have also been presented to establish the utility of the procedure employed.

Chemical element abundance in K giant atmospheres

International Nuclear Information System (INIS)

Komarov, N.S.; Shcherbak, A.N.

1980-01-01

With the help of modified method of differential curves of growth studied are physical parameters of atmospheres of giant stars of KO111 spectral class of the NGC 752, M25 and UMa cluster. Observations have been made on reflector of Crimea astrophysical observatory of Academy of Sciences of the USSR in the period from February to May, 1978. Spectograms are obtained for the wave length range from 5000-5500 A. It is shown that the change of chemical content in the wide range in heavy element composition does not influence the star atmosphere structUre. It follows from the results of the investigation that the abundance of chemical elements in stars of various scattered clusters, is the same in the range of errors of measurements and is similar to the abundance of chemical elements in the Sun atmosphere

Pseudo-potential method for taking into account the Pauli principle in cluster systems

International Nuclear Information System (INIS)

Krasnopol'skii, V.M.; Kukulin, V.I.

1975-01-01

In order to take account of the Pauli principle in cluster systems (such as 3α, α + α + n) a convenient method of renormalization of the cluster-cluster deep attractive potentials with forbidden states is suggested. The renormalization consists of adding projectors upon the occupied states with an infinite coupling constant to the initial deep potential which means that we pass to pseudo-potentials. The pseudo-potential approach in projecting upon the noneigenstates is shown to be equivalent to the orthogonality condition model of Saito et al. The orthogonality of the many-particle wave function to the forbidden states of each two-cluster sub-system is clearly demonstrated

Survey of Nuclear Methods in Chemical Technology

International Nuclear Information System (INIS)

Broda, E.

1966-01-01

An attempt is made to classify nuclear methods on a logical basis to facilitate assimilation by the technologist. The three main groups are: (I) Tracer methods, (II) Methods based on the influence of absorbers on radiations to be measured, and (III) Radiation chemical methods. The variants of the first two groups are discussed in some detail, and typical examples are given. Group I can be subdivided into (1) Indicator methods, (2) Emanation methods, (3) Radioreagent methods, and (4) Isotope dilution methods, Group II into (5) Activation methods, (6) Absorption methods, (7) Induced Nuclear Reaction methods, (8) Scattering methods, and (9) Fluorescence methods. While the economic benefits due to nuclear methods already run into hundreds of millions of dollars annually, owing to radiation protection problems radiochemical methods in the strict sense are not widely used in actual production. It is suggested that more use should be made of pilot plant tracer studies of chemical processes as used in industry. (author)

Test computations on the dynamical evolution of star clusters. [Fluid dynamic method

Energy Technology Data Exchange (ETDEWEB)

Angeletti, L; Giannone, P. (Rome Univ. (Italy))

1977-01-01

Test calculations have been carried out on the evolution of star clusters using the fluid-dynamical method devised by Larson (1970). Large systems of stars have been considered with specific concern with globular clusters. With reference to the analogous 'standard' model by Larson, the influence of varying in turn the various free parameters (cluster mass, star mass, tidal radius, mass concentration of the initial model) has been studied for the results. Furthermore, the partial release of some simplifying assumptions with regard to the relaxation time and distribution of the 'target' stars has been considered. The change of the structural properties is discussed, and the variation of the evolutionary time scale is outlined. An indicative agreement of the results obtained here with structural properties of globular clusters as deduced from previous theoretical models is pointed out.

Exploring the atomic structure of 1.8 nm monolayer-protected gold clusters with aberration-corrected STEM

Energy Technology Data Exchange (ETDEWEB)

Liu, Jian; Jian, Nan; Ornelas, Isabel; Pattison, Alexander J. [Nanoscale Physics Research Laboratory, School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom); Lahtinen, Tanja; Salorinne, Kirsi [Department of Chemistry, Nanoscience Center, University of Jyväskylä, FI-40014 Jyväskylä (Finland); Häkkinen, Hannu [Department of Chemistry, Nanoscience Center, University of Jyväskylä, FI-40014 Jyväskylä (Finland); Department of Physics, Nanoscience Center, University of Jyväskylä, FI-40014 Jyväskylä (Finland); Palmer, Richard E., E-mail: richardepalmerwork@yahoo.com [Nanoscale Physics Research Laboratory, School of Physics and Astronomy, University of Birmingham, Birmingham B15 2TT (United Kingdom)

2017-05-15

Monolayer-protected (MP) Au clusters present attractive quantum systems with a range of potential applications e.g. in catalysis. Knowledge of the atomic structure is needed to obtain a full understanding of their intriguing physical and chemical properties. Here we employed aberration-corrected scanning transmission electron microscopy (ac-STEM), combined with multislice simulations, to make a round-robin investigation of the atomic structure of chemically synthesised clusters with nominal composition Au{sub 144}(SCH{sub 2}CH{sub 2}Ph){sub 60} provided by two different research groups. The MP Au clusters were “weighed” by the atom counting method, based on their integrated intensities in the high angle annular dark field (HAADF) regime and calibrated exponent of the Z dependence. For atomic structure analysis, we compared experimental images of hundreds of clusters, with atomic resolution, against a variety of structural models. Across the size range 123–151 atoms, only 3% of clusters matched the theoretically predicted Au{sub 144}(SR){sub 60} structure, while a large proportion of the clusters were amorphous (i.e. did not match any model structure). However, a distinct ring-dot feature, characteristic of local icosahedral symmetry, was observed in about 20% of the clusters. - Highlights: • Chemically synthesised gold clusters were “weighed” by atom counting to get true size. • Image simulations show a few percent of clusters have the predicted atomic structure. • But a specific ring-dot feature indicates local icosahedral order in many clusters.

Environmental data processing by clustering methods for energy forecast and planning

Energy Technology Data Exchange (ETDEWEB)

Di Piazza, Annalisa [Dipartimento di Ingegneria Idraulica e Applicazioni Ambientali (DIIAA), viale delle Scienze, Universita degli Studi di Palermo, 90128 Palermo (Italy); Di Piazza, Maria Carmela; Ragusa, Antonella; Vitale, Gianpaolo [Consiglio Nazionale delle Ricerche Istituto di Studi sui Sistemi Intelligenti per l' Automazione (ISSIA - CNR), sezione di Palermo, Via Dante, 12, 90141 Palermo (Italy)

2011-03-15

This paper presents a statistical approach based on the k-means clustering technique to manage environmental sampled data to evaluate and to forecast of the energy deliverable by different renewable sources in a given site. In particular, wind speed and solar irradiance sampled data are studied in association to the energy capability of a wind generator and a photovoltaic (PV) plant, respectively. The proposed method allows the sub-sets of useful data, describing the energy capability of a site, to be extracted from a set of experimental observations belonging the considered site. The data collection is performed in Sicily, in the south of Italy, as case study. As far as the wind generation is concerned, a suitable generator, matching the wind profile of the studied sites, has been selected for the evaluation of the producible energy. With respect to the photovoltaic generation, the irradiance data have been taken from the acquisition system of an actual installation. It is demonstrated, in both cases, that the use of the k-means clustering method allows data that do not contribute to the produced energy to be grouped into a cluster, moreover it simplifies the problem of the energy assessment since it permits to obtain the desired information on energy capability by managing a reduced amount of experimental samples. In the studied cases, the proposed method permitted a reduction of the 50% of the data with a maximum discrepancy of 10% in energy estimation compared to the classical statistical approach. Therefore, the adopted k-means clustering technique represents an useful tool for an appropriate and less demanding energy forecast and planning in distributed generation systems. (author)

The use of different clustering methods in the evaluation of genetic diversity in upland cotton

Directory of Open Access Journals (Sweden)

Laíse Ferreira de Araújo

Full Text Available The continuous development and evaluation of new genotypes through crop breeding is essential in order to obtain new cultivars. The objective of this work was to evaluate the genetic divergences between cultivars of upland cotton (Gossypium hirsutum L. using the agronomic and technological characteristics of the fibre, in order to select superior parent plants. The experiment was set up during 2010 at the Federal University of Ceará in Fortaleza, Ceará, Brazil. Eleven cultivars of upland cotton were used in an experimental design of randomised blocks with three replications. In order to evaluate the genetic diversity among cultivars, the generalised Mahalanobis distance matrix was calculated, with cluster analysis then being applied, employing various methods: single linkage, Ward, complete linkage, median, average linkage within a cluster and average linkage between clusters. Genetic variability exists among the evaluated genotypes. The most consistant clustering method was that employing average linkage between clusters. Among the characteristics assessed, mean boll weight presented the highest contribution to genetic diversity, followed by elongation at rupture. Employing the method of mean linkage between clusters, the cultivars with greater genetic divergence were BRS Acacia and LD Frego; those of greater similarity were BRS Itaúba and BRS Araripe.

Comparison Of Keyword Based Clustering Of Web Documents By Using Openstack 4j And By Traditional Method

Directory of Open Access Journals (Sweden)

Shiza Anand

2015-08-01

Full Text Available As the number of hypertext documents are increasing continuously day by day on world wide web. Therefore clustering methods will be required to bind documents into the clusters repositories according to the similarity lying between the documents. Various clustering methods exist such as Hierarchical Based K-means Fuzzy Logic Based Centroid Based etc. These keyword based clustering methods takes much more amount of time for creating containers and putting documents in their respective containers. These traditional methods use File Handling techniques of different programming languages for creating repositories and transferring web documents into these containers. In contrast openstack4j SDK is a new technique for creating containers and shifting web documents into these containers according to the similarity in much more less amount of time as compared to the traditional methods. Another benefit of this technique is that this SDK understands and reads all types of files such as jpg html pdf doc etc. This paper compares the time required for clustering of documents by using openstack4j and by traditional methods and suggests various search engines to adopt this technique for clustering so that they give result to the user querries in less amount of time.

Application of clustering methods: Regularized Markov clustering (R-MCL) for analyzing dengue virus similarity

Science.gov (United States)

Lestari, D.; Raharjo, D.; Bustamam, A.; Abdillah, B.; Widhianto, W.

2017-07-01

Dengue virus consists of 10 different constituent proteins and are classified into 4 major serotypes (DEN 1 - DEN 4). This study was designed to perform clustering against 30 protein sequences of dengue virus taken from Virus Pathogen Database and Analysis Resource (VIPR) using Regularized Markov Clustering (R-MCL) algorithm and then we analyze the result. By using Python program 3.4, R-MCL algorithm produces 8 clusters with more than one centroid in several clusters. The number of centroid shows the density level of interaction. Protein interactions that are connected in a tissue, form a complex protein that serves as a specific biological process unit. The analysis of result shows the R-MCL clustering produces clusters of dengue virus family based on the similarity role of their constituent protein, regardless of serotypes.

The cosmological analysis of X-ray cluster surveys - I. A new method for interpreting number counts

Science.gov (United States)

Clerc, N.; Pierre, M.; Pacaud, F.; Sadibekova, T.

2012-07-01

We present a new method aimed at simplifying the cosmological analysis of X-ray cluster surveys. It is based on purely instrumental observable quantities considered in a two-dimensional X-ray colour-magnitude diagram (hardness ratio versus count rate). The basic principle is that even in rather shallow surveys, substantial information on cluster redshift and temperature is present in the raw X-ray data and can be statistically extracted; in parallel, such diagrams can be readily predicted from an ab initio cosmological modelling. We illustrate the methodology for the case of a 100-deg2XMM survey having a sensitivity of ˜10-14 erg s-1 cm-2 and fit at the same time, the survey selection function, the cluster evolutionary scaling relations and the cosmology; our sole assumption - driven by the limited size of the sample considered in the case study - is that the local cluster scaling relations are known. We devote special attention to the realistic modelling of the count-rate measurement uncertainties and evaluate the potential of the method via a Fisher analysis. In the absence of individual cluster redshifts, the count rate and hardness ratio (CR-HR) method appears to be much more efficient than the traditional approach based on cluster counts (i.e. dn/dz, requiring redshifts). In the case where redshifts are available, our method performs similar to the traditional mass function (dn/dM/dz) for the purely cosmological parameters, but constrains better parameters defining the cluster scaling relations and their evolution. A further practical advantage of the CR-HR method is its simplicity: this fully top-down approach totally bypasses the tedious steps consisting in deriving cluster masses from X-ray temperature measurements.

Safety in the Chemical Laboratory: Tested Disposal Methods for Chemical Wastes from Academic Laboratories.

Science.gov (United States)

Armour, M. A.; And Others

1985-01-01

Describes procedures for disposing of dichromate cleaning solution, picric acid, organic azides, oxalic acid, chemical spills, and hydroperoxides in ethers and alkenes. These methods have been tested under laboratory conditions and are specific for individual chemicals rather than for groups of chemicals. (JN)

Cluster detection methods applied to the Upper Cape Cod cancer data

Directory of Open Access Journals (Sweden)

Ozonoff David

2005-09-01

Full Text Available Abstract Background A variety of statistical methods have been suggested to assess the degree and/or the location of spatial clustering of disease cases. However, there is relatively little in the literature devoted to comparison and critique of different methods. Most of the available comparative studies rely on simulated data rather than real data sets. Methods We have chosen three methods currently used for examining spatial disease patterns: the M-statistic of Bonetti and Pagano; the Generalized Additive Model (GAM method as applied by Webster; and Kulldorff's spatial scan statistic. We apply these statistics to analyze breast cancer data from the Upper Cape Cancer Incidence Study using three different latency assumptions. Results The three different latency assumptions produced three different spatial patterns of cases and controls. For 20 year latency, all three methods generally concur. However, for 15 year latency and no latency assumptions, the methods produce different results when testing for global clustering. Conclusion The comparative analyses of real data sets by different statistical methods provides insight into directions for further research. We suggest a research program designed around examining real data sets to guide focused investigation of relevant features using simulated data, for the purpose of understanding how to interpret statistical methods applied to epidemiological data with a spatial component.

Smoothed Particle Inference: A Kilo-Parametric Method for X-ray Galaxy Cluster Modeling

Energy Technology Data Exchange (ETDEWEB)

Peterson, John R.; Marshall, P.J.; /KIPAC, Menlo Park; Andersson, K.; /Stockholm U. /SLAC

2005-08-05

We propose an ambitious new method that models the intracluster medium in clusters of galaxies as a set of X-ray emitting smoothed particles of plasma. Each smoothed particle is described by a handful of parameters including temperature, location, size, and elemental abundances. Hundreds to thousands of these particles are used to construct a model cluster of galaxies, with the appropriate complexity estimated from the data quality. This model is then compared iteratively with X-ray data in the form of adaptively binned photon lists via a two-sample likelihood statistic and iterated via Markov Chain Monte Carlo. The complex cluster model is propagated through the X-ray instrument response using direct sampling Monte Carlo methods. Using this approach the method can reproduce many of the features observed in the X-ray emission in a less assumption-dependent way that traditional analyses, and it allows for a more detailed characterization of the density, temperature, and metal abundance structure of clusters. Multi-instrument X-ray analyses and simultaneous X-ray, Sunyaev-Zeldovich (SZ), and lensing analyses are a straight-forward extension of this methodology. Significant challenges still exist in understanding the degeneracy in these models and the statistical noise induced by the complexity of the models.

Framework methodology for increased energy efficiency and renewable feedstock integration in industrial clusters

International Nuclear Information System (INIS)

Hackl, Roman; Harvey, Simon

2013-01-01

Highlights: • Framework methodology for energy efficiency of process plants and total sites. • Identification of suitable biorefinery based on host site future energy systems. • Case study results show large energy savings of site wide heat integration. • Case study on refrigeration systems: 15% shaft work savings potential. • Case study on biorefinery integration: utility savings potential of up to 37%. - Abstract: Energy intensive industries, such as the bulk chemical industry, are facing major challenges and adopting strategies to face these challenges. This paper investigates options for clusters of chemical process plants to decrease their energy and emission footprints. There is a wide range of technologies and process integration opportunities available for achieving these objectives, including (i) decreasing fossil fuel and electricity demand by increasing heat integration within individual processes and across the total cluster site; (ii) replacing fossil feedstocks with renewables and biorefinery integration with the existing cluster; (iii) increasing external utilization of excess process heat wherever possible. This paper presents an overview of the use of process integration methods for development of chemical clusters. Process simulation, pinch analysis, Total Site Analysis (TSA) and exergy concepts are combined in a holistic approach to identify opportunities to improve energy efficiency and integrate renewable feedstocks within such clusters. The methodology is illustrated by application to a chemical cluster in Stenungsund on the West Coast of Sweden consisting of five different companies operating six process plants. The paper emphasizes and quantifies the gains that can be made by adopting a total site approach for targeting energy efficiency measures within the cluster and when investigating integration opportunities for advanced biorefinery concepts compared to restricting the analysis to the individual constituent plants. The

Persistent local chemical bonds in intermetallic phase formation

Energy Technology Data Exchange (ETDEWEB)

Bai, Yanwen [Key Laboratory for Liquid–Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); Bian, Xiufang, E-mail: xfbian@sdu.edu.cn [Key Laboratory for Liquid–Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); Qin, Xubo [Key Laboratory for Liquid–Solid Structural Evolution and Processing of Materials, Ministry of Education, Shandong University, Jinan 250061 (China); Zhang, Shuo; Huang, Yuying [Shanghai Synchrotron Radiation Facilities, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201204 (China)

2014-05-01

We found a direct evidence for the existence of the local chemical Bi–In bonds in the BiIn{sub 2} melt. These bonds are strong and prevail, dominating the structure evolution of the intermetallic clusters. From the local structure of the melt-quenched BiIn{sub 2} ribbon, the chemical Bi–In bonds strengthen compared with those in the equilibrium solidified alloy. The chemical bonds in BiIn{sub 2} melt retain to solid during a rapid quenching process. The results suggest that the intermetallic clusters in the melt evolve into the as-quenched intermetallic phase, and the intermetallic phase originates from the chemical bonds between unlike atoms in the melt. The chemical bonds preserve the chemical ordered clusters and dominate the clusters evolution.

Form gene clustering method about pan-ethnic-group products based on emotional semantic

Science.gov (United States)

Chen, Dengkai; Ding, Jingjing; Gao, Minzhuo; Ma, Danping; Liu, Donghui

2016-09-01

The use of pan-ethnic-group products form knowledge primarily depends on a designer's subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers' perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.

Chemical decontaminating method for stainless steel

International Nuclear Information System (INIS)

Onuma, Tsutomu; Akimoto, Hidetoshi.

1990-01-01

Radioactive metal wastes comprising passivated stainless steels are chemically decontaminated to such a radioactivity level as that of usual wastes. The present invention for chemically decontaminating stainless steels comprises a first step of immersing decontaminates into a sulfuric acid solution and a second step of immersing them into an aqueous solution prepared by adding oxidative metal salts to sulfuric acid, in which a portion of the surface of stainless steels as decontaminates are chemically ground to partially expose substrate materials and then the above-mentioned decontamination steps are applied. More than 90% of radioactive materials are removed in this method by the dissolution of the exposed substrate materials and peeling of cruds secured to the surface of the materials upon dissolution. This method is applicable to decontamination of articles having complicate shapes, can reduce the amount of secondary wastes after decontamination and also remarkably shorten the time required for decontamination. (T.M.)

Determining wood chip size: image analysis and clustering methods

Directory of Open Access Journals (Sweden)

Paolo Febbi

2013-09-01

Full Text Available One of the standard methods for the determination of the size distribution of wood chips is the oscillating screen method (EN 15149- 1:2010. Recent literature demonstrated how image analysis could return highly accurate measure of the dimensions defined for each individual particle, and could promote a new method depending on the geometrical shape to determine the chip size in a more accurate way. A sample of wood chips (8 litres was sieved through horizontally oscillating sieves, using five different screen hole diameters (3.15, 8, 16, 45, 63 mm; the wood chips were sorted in decreasing size classes and the mass of all fractions was used to determine the size distribution of the particles. Since the chip shape and size influence the sieving results, Wang’s theory, which concerns the geometric forms, was considered. A cluster analysis on the shape descriptors (Fourier descriptors and size descriptors (area, perimeter, Feret diameters, eccentricity was applied to observe the chips distribution. The UPGMA algorithm was applied on Euclidean distance. The obtained dendrogram shows a group separation according with the original three sieving fractions. A comparison has been made between the traditional sieve and clustering results. This preliminary result shows how the image analysis-based method has a high potential for the characterization of wood chip size distribution and could be further investigated. Moreover, this method could be implemented in an online detection machine for chips size characterization. An improvement of the results is expected by using supervised multivariate methods that utilize known class memberships. The main objective of the future activities will be to shift the analysis from a 2-dimensional method to a 3- dimensional acquisition process.

Implementation of K-Means Clustering Method for Electronic Learning Model

Science.gov (United States)

Latipa Sari, Herlina; Suranti Mrs., Dewi; Natalia Zulita, Leni

2017-12-01

Teaching and Learning process at SMK Negeri 2 Bengkulu Tengah has applied e-learning system for teachers and students. The e-learning was based on the classification of normative, productive, and adaptive subjects. SMK Negeri 2 Bengkulu Tengah consisted of 394 students and 60 teachers with 16 subjects. The record of e-learning database was used in this research to observe students’ activity pattern in attending class. K-Means algorithm in this research was used to classify students’ learning activities using e-learning, so that it was obtained cluster of students’ activity and improvement of student’s ability. Implementation of K-Means Clustering method for electronic learning model at SMK Negeri 2 Bengkulu Tengah was conducted by observing 10 students’ activities, namely participation of students in the classroom, submit assignment, view assignment, add discussion, view discussion, add comment, download course materials, view article, view test, and submit test. In the e-learning model, the testing was conducted toward 10 students that yielded 2 clusters of membership data (C1 and C2). Cluster 1: with membership percentage of 70% and it consisted of 6 members, namely 1112438 Anggi Julian, 1112439 Anis Maulita, 1112441 Ardi Febriansyah, 1112452 Berlian Sinurat, 1112460 Dewi Anugrah Anwar and 1112467 Eka Tri Oktavia Sari. Cluster 2:with membership percentage of 30% and it consisted of 4 members, namely 1112463 Dosita Afriyani, 1112471 Erda Novita, 1112474 Eskardi and 1112477 Fachrur Rozi.

A Detailed Study of Chemical Enrichment History of Galaxy Clusters out to Virial Radius

Science.gov (United States)

Loewenstein, Michael

The origin of the metal enrichment of the intracluster medium (ICM) represents a fundamental problem in extragalactic astrophysics, with implications for our understanding of how stars and galaxies form, the nature of Type Ia supernova (SNIa) progenitors, and the thermal history of the ICM. These heavy elements are ultimately synthesized by supernova (SN) explosions; however, the details of the sites of metal production and mechanisms that transport metals to the ICM remain unclear. To make progress, accurate abundance profiles for multiple elements extending from the cluster core out to the virial radius (r180) are required for a significant cluster sample. We propose an X-ray spectroscopic study of a carefully-chosen sample of archival Suzaku and XMM-Newton observations of 23 clusters: XMM-Newton data probe the cluster temperature and abundances out to (0.5-1)r500, while Suzaku data probe the cluster outskirts. A method devised by our team to utilize all elements with emission lines in the X-ray bandpass to measure the relative contributions of supernova explosions by direct modeling of their X-ray spectra will be applied in order to constrain the demographics of the enriching supernova population. In addition we will conduct a stacking analysis of our already existing Suzaku and XMM-Newton cluster spectra to search for weak emssion lines that are important SN diagnostics, and to look for trends with cluster mass and redshift. The funding we propose here will also support the data analysis of our recent Suzaku observations of the archetypal cluster A3112 (200 ks each on the core and outskirts). Our data analysis, intepreted using theoretical models we have developed, will enable us to constrain the star formation history, SN demographics, and nature of SNIa progenitors associated with galaxy cluster stellar populations - and, hence, directly addresess NASA s Strategic Objective 2.4.2 in Astrophysics that aims to improve the understanding of how the Universe works

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

Science.gov (United States)

Brusco, Michael J; Shireman, Emilie; Steinley, Douglas

2017-09-01

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

THERMAL AND CHEMICAL EVOLUTIONS OF GALAXY CLUSTERS OBSERVED WITH SUZAKU

Directory of Open Access Journals (Sweden)

Kosuke Sato

2013-12-01

Full Text Available We studied the properties of the intracluster medium (ICM of galaxy clusters to outer regions observed with Suzaku. The observed temperature dropped by about ~30% from the central region to the virial radius of the clusters. The derived entropy profile agreed with the expectation from simulations within r500, while the entropy profile in r > r500 indicated a flatter slope than the simulations. This would suggest that the cluster outskirts were out of hydrostatic equilibrium. As for the metallicity, we studied the metal abundances from O to Fe up to ~0.5 times the virial radius of galaxy groups and clusters. Comparing the results with supernova nucleosynthesis models, the number ratio of type II to Ia supernovae is estimated to be ~3.5. We also calculated not only Fe, but also O and Mg mass-to-light ratios (MLRs with K-band luminosity. The MLRs in the clusters had a similar feature.

Open-Source Sequence Clustering Methods Improve the State Of the Art.

Science.gov (United States)

Kopylova, Evguenia; Navas-Molina, Jose A; Mercier, Céline; Xu, Zhenjiang Zech; Mahé, Frédéric; He, Yan; Zhou, Hong-Wei; Rognes, Torbjørn; Caporaso, J Gregory; Knight, Rob

2016-01-01

Sequence clustering is a common early step in amplicon-based microbial community analysis, when raw sequencing reads are clustered into operational taxonomic units (OTUs) to reduce the run time of subsequent analysis steps. Here, we evaluated the performance of recently released state-of-the-art open-source clustering software products, namely, OTUCLUST, Swarm, SUMACLUST, and SortMeRNA, against current principal options (UCLUST and USEARCH) in QIIME, hierarchical clustering methods in mothur, and USEARCH's most recent clustering algorithm, UPARSE. All the latest open-source tools showed promising results, reporting up to 60% fewer spurious OTUs than UCLUST, indicating that the underlying clustering algorithm can vastly reduce the number of these derived OTUs. Furthermore, we observed that stringent quality filtering, such as is done in UPARSE, can cause a significant underestimation of species abundance and diversity, leading to incorrect biological results. Swarm, SUMACLUST, and SortMeRNA have been included in the QIIME 1.9.0 release. IMPORTANCE Massive collections of next-generation sequencing data call for fast, accurate, and easily accessible bioinformatics algorithms to perform sequence clustering. A comprehensive benchmark is presented, including open-source tools and the popular USEARCH suite. Simulated, mock, and environmental communities were used to analyze sensitivity, selectivity, species diversity (alpha and beta), and taxonomic composition. The results demonstrate that recent clustering algorithms can significantly improve accuracy and preserve estimated diversity without the application of aggressive filtering. Moreover, these tools are all open source, apply multiple levels of multithreading, and scale to the demands of modern next-generation sequencing data, which is essential for the analysis of massive multidisciplinary studies such as the Earth Microbiome Project (EMP) (J. A. Gilbert, J. K. Jansson, and R. Knight, BMC Biol 12:69, 2014, http

Evolution of Chemical Diversity in a Group of Non-Reduced Polyketide Gene Clusters: Using Phylogenetics to Inform the Search for Novel Fungal Natural Products

Directory of Open Access Journals (Sweden)

Kurt Throckmorton

2015-09-01

Full Text Available Fungal polyketides are a diverse class of natural products, or secondary metabolites (SMs, with a wide range of bioactivities often associated with toxicity. Here, we focus on a group of non-reducing polyketide synthases (NR-PKSs in the fungal phylum Ascomycota that lack a thioesterase domain for product release, group V. Although widespread in ascomycete taxa, this group of NR-PKSs is notably absent in the mycotoxigenic genus Fusarium and, surprisingly, found in genera not known for their secondary metabolite production (e.g., the mycorrhizal genus Oidiodendron, the powdery mildew genus Blumeria, and the causative agent of white-nose syndrome in bats, Pseudogymnoascus destructans. This group of NR-PKSs, in association with the other enzymes encoded by their gene clusters, produces a variety of different chemical classes including naphthacenediones, anthraquinones, benzophenones, grisandienes, and diphenyl ethers. We discuss the modification of and transitions between these chemical classes, the requisite enzymes, and the evolution of the SM gene clusters that encode them. Integrating this information, we predict the likely products of related but uncharacterized SM clusters, and we speculate upon the utility of these classes of SMs as virulence factors or chemical defenses to various plant, animal, and insect pathogens, as well as mutualistic fungi.

A method for determining the radius of an open cluster from stellar proper motions

Science.gov (United States)

Sánchez, Néstor; Alfaro, Emilio J.; López-Martínez, Fátima

2018-04-01

We propose a method for calculating the radius of an open cluster in an objective way from an astrometric catalogue containing, at least, positions and proper motions. It uses the minimum spanning tree in the proper motion space to discriminate cluster stars from field stars and it quantifies the strength of the cluster-field separation by means of a statistical parameter defined for the first time in this paper. This is done for a range of different sampling radii from where the cluster radius is obtained as the size at which the best cluster-field separation is achieved. The novelty of this strategy is that the cluster radius is obtained independently of how its stars are spatially distributed. We test the reliability and robustness of the method with both simulated and real data from a well-studied open cluster (NGC 188), and apply it to UCAC4 data for five other open clusters with different catalogued radius values. NGC 188, NGC 1647, NGC 6603, and Ruprecht 155 yielded unambiguous radius values of 15.2 ± 1.8, 29.4 ± 3.4, 4.2 ± 1.7, and 7.0 ± 0.3 arcmin, respectively. ASCC 19 and Collinder 471 showed more than one possible solution, but it is not possible to know whether this is due to the involved uncertainties or due to the presence of complex patterns in their proper motion distributions, something that could be inherent to the physical object or due to the way in which the catalogue was sampled.

TreeCluster: Massively scalable transmission clustering using phylogenetic trees

OpenAIRE

Moshiri, Alexander

2018-01-01

Background: The ability to infer transmission clusters from molecular data is critical to designing and evaluating viral control strategies. Viral sequencing datasets are growing rapidly, but standard methods of transmission cluster inference do not scale well beyond thousands of sequences. Results: I present TreeCluster, a cross-platform tool that performs transmission cluster inference on a given phylogenetic tree orders of magnitude faster than existing inference methods and supports multi...

A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

International Nuclear Information System (INIS)

Azimi, R.; Ghayekhloo, M.; Ghofrani, M.

2016-01-01

Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

Soil chemical sensor and precision agricultural chemical delivery system and method

Science.gov (United States)

Colburn, Jr., John W.

1991-01-01

A real time soil chemical sensor and precision agricultural chemical delivery system includes a plurality of ground-engaging tools in association with individual soil sensors which measure soil chemical levels. The system includes the addition of a solvent which rapidly saturates the soil/tool interface to form a conductive solution of chemicals leached from the soil. A multivalent electrode, positioned within a multivalent frame of the ground-engaging tool, applies a voltage or impresses a current between the electrode and the tool frame. A real-time soil chemical sensor and controller senses the electrochemical reaction resulting from the application of the voltage or current to the leachate, measures it by resistivity methods, and compares it against pre-set resistivity levels for substances leached by the solvent. Still greater precision is obtained by calibrating for the secondary current impressed through solvent-less soil. The appropriate concentration is then found and the servo-controlled delivery system applies the appropriate amount of fertilizer or agricultural chemicals substantially in the location from which the soil measurement was taken.

STAR CLUSTERS IN M31. IV. A COMPARATIVE ANALYSIS OF ABSORPTION LINE INDICES IN OLD M31 AND MILKY WAY CLUSTERS

International Nuclear Information System (INIS)

Schiavon, Ricardo P.; Caldwell, Nelson; Morrison, Heather; Harding, Paul; Courteau, Stéphane; MacArthur, Lauren A.; Graves, Genevieve J.

2012-01-01

We present absorption line indices measured in the integrated spectra of globular clusters both from the Galaxy and from M31. Our samples include 41 Galactic globular clusters, and more than 300 clusters in M31. The conversion of instrumental equivalent widths into the Lick system is described, and zero-point uncertainties are provided. Comparison of line indices of old M31 clusters and Galactic globular clusters suggests an absence of important differences in chemical composition between the two cluster systems. In particular, CN indices in the spectra of M31 and Galactic clusters are essentially consistent with each other, in disagreement with several previous works. We reanalyze some of the previous data, and conclude that reported CN differences between M31 and Galactic clusters were mostly due to data calibration uncertainties. Our data support the conclusion that the chemical compositions of Milky Way and M31 globular clusters are not substantially different, and that there is no need to resort to enhanced nitrogen abundances to account for the optical spectra of M31 globular clusters.

STAR CLUSTERS IN M31. IV. A COMPARATIVE ANALYSIS OF ABSORPTION LINE INDICES IN OLD M31 AND MILKY WAY CLUSTERS

Energy Technology Data Exchange (ETDEWEB)

Schiavon, Ricardo P. [Gemini Observatory, Hilo, HI 96720 (United States); Caldwell, Nelson [Smithsonian Astrophysical Observatory, Cambridge, MA 02138 (United States); Morrison, Heather; Harding, Paul [Department of Astronomy, Case Western Reserve University, Cleveland, OH 44106-7215 (United States); Courteau, Stephane [Department of Physics, Engineering Physics and Astronomy, Queen' s University, Kingston, ON K7L 3N6 (Canada); MacArthur, Lauren A. [Herzberg Institute of Astrophysics, National Research Council of Canada/University of Victoria, Victoria, B.C. V9E 2E7 (Canada); Graves, Genevieve J., E-mail: rschiavon@gemini.edu, E-mail: caldwell@cfa.harvard.edu, E-mail: paul.harding@case.edu, E-mail: heather@vegemite.case.edu, E-mail: courteau@astro.queensu.ca, E-mail: Lauren.MacArthur@nrc-cnrc.gc.ca, E-mail: graves@astro.berkeley.edu [Department of Astronomy, University of California, Berkeley, CA 94720 (United States)

2012-01-15

We present absorption line indices measured in the integrated spectra of globular clusters both from the Galaxy and from M31. Our samples include 41 Galactic globular clusters, and more than 300 clusters in M31. The conversion of instrumental equivalent widths into the Lick system is described, and zero-point uncertainties are provided. Comparison of line indices of old M31 clusters and Galactic globular clusters suggests an absence of important differences in chemical composition between the two cluster systems. In particular, CN indices in the spectra of M31 and Galactic clusters are essentially consistent with each other, in disagreement with several previous works. We reanalyze some of the previous data, and conclude that reported CN differences between M31 and Galactic clusters were mostly due to data calibration uncertainties. Our data support the conclusion that the chemical compositions of Milky Way and M31 globular clusters are not substantially different, and that there is no need to resort to enhanced nitrogen abundances to account for the optical spectra of M31 globular clusters.

Symptom Clusters in Advanced Cancer Patients: An Empirical Comparison of Statistical Methods and the Impact on Quality of Life.

Science.gov (United States)

Dong, Skye T; Costa, Daniel S J; Butow, Phyllis N; Lovell, Melanie R; Agar, Meera; Velikova, Galina; Teckle, Paulos; Tong, Allison; Tebbutt, Niall C; Clarke, Stephen J; van der Hoek, Kim; King, Madeleine T; Fayers, Peter M

2016-01-01

Symptom clusters in advanced cancer can influence patient outcomes. There is large heterogeneity in the methods used to identify symptom clusters. To investigate the consistency of symptom cluster composition in advanced cancer patients using different statistical methodologies for all patients across five primary cancer sites, and to examine which clusters predict functional status, a global assessment of health and global quality of life. Principal component analysis and exploratory factor analysis (with different rotation and factor selection methods) and hierarchical cluster analysis (with different linkage and similarity measures) were used on a data set of 1562 advanced cancer patients who completed the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire-Core 30. Four clusters consistently formed for many of the methods and cancer sites: tense-worry-irritable-depressed (emotional cluster), fatigue-pain, nausea-vomiting, and concentration-memory (cognitive cluster). The emotional cluster was a stronger predictor of overall quality of life than the other clusters. Fatigue-pain was a stronger predictor of overall health than the other clusters. The cognitive cluster and fatigue-pain predicted physical functioning, role functioning, and social functioning. The four identified symptom clusters were consistent across statistical methods and cancer types, although there were some noteworthy differences. Statistical derivation of symptom clusters is in need of greater methodological guidance. A psychosocial pathway in the management of symptom clusters may improve quality of life. Biological mechanisms underpinning symptom clusters need to be delineated by future research. A framework for evidence-based screening, assessment, treatment, and follow-up of symptom clusters in advanced cancer is essential. Copyright © 2016 American Academy of Hospice and Palliative Medicine. Published by Elsevier Inc. All rights reserved.

Fuzzy Clustering Methods and their Application to Fuzzy Modeling

DEFF Research Database (Denmark)

Kroszynski, Uri; Zhou, Jianjun

1999-01-01

Fuzzy modeling techniques based upon the analysis of measured input/output data sets result in a set of rules that allow to predict system outputs from given inputs. Fuzzy clustering methods for system modeling and identification result in relatively small rule-bases, allowing fast, yet accurate....... An illustrative synthetic example is analyzed, and prediction accuracy measures are compared between the different variants...

A comparison of three clustering methods for finding subgroups in MRI, SMS or clinical data: SPSS TwoStep Cluster analysis, Latent Gold and SNOB.

Science.gov (United States)

Kent, Peter; Jensen, Rikke K; Kongsted, Alice

2014-10-02

There are various methodological approaches to identifying clinically important subgroups and one method is to identify clusters of characteristics that differentiate people in cross-sectional and/or longitudinal data using Cluster Analysis (CA) or Latent Class Analysis (LCA). There is a scarcity of head-to-head comparisons that can inform the choice of which clustering method might be suitable for particular clinical datasets and research questions. Therefore, the aim of this study was to perform a head-to-head comparison of three commonly available methods (SPSS TwoStep CA, Latent Gold LCA and SNOB LCA). The performance of these three methods was compared: (i) quantitatively using the number of subgroups detected, the classification probability of individuals into subgroups, the reproducibility of results, and (ii) qualitatively using subjective judgments about each program's ease of use and interpretability of the presentation of results.We analysed five real datasets of varying complexity in a secondary analysis of data from other research projects. Three datasets contained only MRI findings (n = 2,060 to 20,810 vertebral disc levels), one dataset contained only pain intensity data collected for 52 weeks by text (SMS) messaging (n = 1,121 people), and the last dataset contained a range of clinical variables measured in low back pain patients (n = 543 people). Four artificial datasets (n = 1,000 each) containing subgroups of varying complexity were also analysed testing the ability of these clustering methods to detect subgroups and correctly classify individuals when subgroup membership was known. The results from the real clinical datasets indicated that the number of subgroups detected varied, the certainty of classifying individuals into those subgroups varied, the findings had perfect reproducibility, some programs were easier to use and the interpretability of the presentation of their findings also varied. The results from the artificial datasets

Mass spectrometric production of heterogeneous metal clusters using Knudsen cell

Directory of Open Access Journals (Sweden)

Veljković Filip M.

2016-01-01

Full Text Available Knudsen effusion mass spectrometry or high-temperature method of mass spectrometry for decades gives new information about saturated vapor of hardly volatile compounds and it is an important method in the discovery of many new molecules, radicals, ions and clusters present in the gas phase. Since pioneering works until now, this method has been successfully applied to a large number of systems (ores, oxides, ceramics, glass materials, borides, carbides, sulfides, nitrates, metals, fullerenes, etc which led to the establishment of various research branches such as chemistry of clusters. This paper describes the basic principles of Knudsen cell use for both identification of chemical species created in the process of evaporation and determination of their ionization energies. Depending on detected ions intensities and the partial pressure of each gaseous component, as well as on changes in partial pressure with temperature, Knudsen cell mass spectrometry enables the determination of thermodynamic parameters of the tested system. A special attention is paid to its application in the field of small heterogeneous and homogeneous clusters of alkali metals. Furthermore, experimental results for thermodynamic parameters of some clusters, as well as capabilities of non-standard ways of using Knudsen cells in the process of synthesis of new clusters are presented herein. [Projekat Ministarstva nauke Republike Srbije, br. 172019

Applying Clustering Methods in Drawing Maps of Science: Case Study of the Map For Urban Management Science

Directory of Open Access Journals (Sweden)

Mohammad Abuei Ardakan

2010-04-01

Full Text Available The present paper offers a basic introduction to data clustering and demonstrates the application of clustering methods in drawing maps of science. All approaches towards classification and clustering of information are briefly discussed. Their application to the process of visualization of conceptual information and drawing of science maps are illustrated by reviewing similar researches in this field. By implementing aggregated hierarchical clustering algorithm, which is an algorithm based on complete-link method, the map for urban management science as an emerging, interdisciplinary scientific field is analyzed and reviewed.

A study of several CAD methods for classification of clustered microcalcifications

Science.gov (United States)

Wei, Liyang; Yang, Yongyi; Nishikawa, Robert M.; Jiang, Yulei

2005-04-01

In this paper we investigate several state-of-the-art machine-learning methods for automated classification of clustered microcalcifications (MCs), aimed to assisting radiologists for more accurate diagnosis of breast cancer in a computer-aided diagnosis (CADx) scheme. The methods we consider include: support vector machine (SVM), kernel Fisher discriminant (KFD), and committee machines (ensemble averaging and AdaBoost), most of which have been developed recently in statistical learning theory. We formulate differentiation of malignant from benign MCs as a supervised learning problem, and apply these learning methods to develop the classification algorithms. As input, these methods use image features automatically extracted from clustered MCs. We test these methods using a database of 697 clinical mammograms from 386 cases, which include a wide spectrum of difficult-to-classify cases. We use receiver operating characteristic (ROC) analysis to evaluate and compare the classification performance by the different methods. In addition, we also investigate how to combine information from multiple-view mammograms of the same case so that the best decision can be made by a classifier. In our experiments, the kernel-based methods (i.e., SVM, KFD) yield the best performance, significantly outperforming a well-established CADx approach based on neural network learning.

Chemical inhomogeneity in In{sub x}Ga{sub 1-x}N and ZnO. A HRTEM study on atomic scale clustering

Energy Technology Data Exchange (ETDEWEB)

Bartel, T.P.

2008-10-08

Nanostructuration as well as the nucleation and growth of nanoparticles pervades the development of modern materials and devices. Quantitative high resolution transmission electron microscopy (HRTEM) is currently being developed for a structural and chemical analysis at an atomic scale. It is used in this thesis to study the chemical inhomogeneity and clustering in In{sub x}Ga{sub 1-x}N, InN and ZnO. A methodology for reliable quantitative HRTEM is rst de ned: it necessitates a damage free sample, the avoidance of electron beam damage and the control of microscope instabilities. With these conditions satis ed, the reliability of quantitative HRTEM is demonstrated by an accurate measurement of lattice relaxation in a thin TEM sample. Clustering in an alloy can then be distinguished from a random distribution of atoms. In In{sub x}Ga{sub 1-x}N for instance, clustering is detected for concentrations x>0.1. The sensitivity is insufficient to determine whether clustering is present for lower concentrations. HRTEM allows to identify the amplitude and the spatial distribution of the decomposition which is attributed to a spinodal decomposition. In InN, nanometer scale metallic indium inclusions are detected. With decreasing size of the metallic clusters, the photoluminescence of the sample shifts towards the infrared. This indicates that the inclusions may be responsible for the infrared activity of InN. Finally, ZnO grown homoepitaxially on zinc-face and oxygen-face substrates is studied. The O-face epilayer is strained whereas the Zn-face epilayer is almost strain free and has a higher crystalline quality. Quantitative analysis of exit wave phases is in good agreement with simulations, but the signal to noise ratio needs to be improved for the detection of single point defects. (orig.)

The incidence of chemically peculiar stars in open clusters

Energy Technology Data Exchange (ETDEWEB)

Netopil, M.

2013-07-01

The formation and evolution of chemically peculiar (CP) stars is still a matter of debate, although the first representatives were detected already more than 110 years ago. From the astrophysical point of view, these objects are of particular interest, since they combine a wide variety of specific properties. This work deals with a subgroup of CP stars, showing beside overabundances of some iron-peak (e.g. Chromium) and rare earth elements like Europium, also strong ordered magnetic fields as well as a slow rotation. Based on a selection of well investigated close field stars, the possible connections of these properties were examined. However, the main research focus was the analysis of a relationship between the number of CP stars and their age. For this purpose, the results of the extensive Delta-a survey in open clusters were used. This photometric filter system allows an efficient and economic detection of magnetic CP stars due to a characteristic flux depression at 520 nm. Compared to field stars, open clusters offer the big advantage that for example the age and the distance can be determined more accurately on a statistical basis, since all member stars of an open cluster originate more or less at the same time from a single molecular cloud. After the membership analysis of all in Delta a investigated objects and the determination of the cluster parameters, it was shown that the occurrence of CP stars depend on age, but can be explained by the general evolution of stars. This entails amongst others that CP stars show their typical characteristics already as soon as they have arrived the main-sequence, or even before. Furthermore, we were able to detect at least one representative, probably still in its pre-main-sequence phase. (author) [German] Die Entstehung und Entwicklung von chemisch pekuliaren (CP) Sternen ist nach wie vor nicht restlos geklärt, und das obwohl die ersten Vertreter dieser Sterngruppe bereits vor mehr als 110 Jahren entdeckt wurden. Aus

Physical-chemical property based sequence motifs and methods regarding same

Science.gov (United States)

Braun, Werner [Friendswood, TX; Mathura, Venkatarajan S [Sarasota, FL; Schein, Catherine H [Friendswood, TX

2008-09-09

A data analysis system, program, and/or method, e.g., a data mining/data exploration method, using physical-chemical property motifs. For example, a sequence database may be searched for identifying segments thereof having physical-chemical properties similar to the physical-chemical property motifs.

Clustering Dycom

KAUST Repository

Minku, Leandro L.

2017-10-06

Background: Software Effort Estimation (SEE) can be formulated as an online learning problem, where new projects are completed over time and may become available for training. In this scenario, a Cross-Company (CC) SEE approach called Dycom can drastically reduce the number of Within-Company (WC) projects needed for training, saving the high cost of collecting such training projects. However, Dycom relies on splitting CC projects into different subsets in order to create its CC models. Such splitting can have a significant impact on Dycom\\'s predictive performance. Aims: This paper investigates whether clustering methods can be used to help finding good CC splits for Dycom. Method: Dycom is extended to use clustering methods for creating the CC subsets. Three different clustering methods are investigated, namely Hierarchical Clustering, K-Means, and Expectation-Maximisation. Clustering Dycom is compared against the original Dycom with CC subsets of different sizes, based on four SEE databases. A baseline WC model is also included in the analysis. Results: Clustering Dycom with K-Means can potentially help to split the CC projects, managing to achieve similar or better predictive performance than Dycom. However, K-Means still requires the number of CC subsets to be pre-defined, and a poor choice can negatively affect predictive performance. EM enables Dycom to automatically set the number of CC subsets while still maintaining or improving predictive performance with respect to the baseline WC model. Clustering Dycom with Hierarchical Clustering did not offer significant advantage in terms of predictive performance. Conclusion: Clustering methods can be an effective way to automatically generate Dycom\\'s CC subsets.

Water Quality Evaluation of the Yellow River Basin Based on Gray Clustering Method

Science.gov (United States)

Fu, X. Q.; Zou, Z. H.

2018-03-01

Evaluating the water quality of 12 monitoring sections in the Yellow River Basin comprehensively by grey clustering method based on the water quality monitoring data from the Ministry of environmental protection of China in May 2016 and the environmental quality standard of surface water. The results can reflect the water quality of the Yellow River Basin objectively. Furthermore, the evaluation results are basically the same when compared with the fuzzy comprehensive evaluation method. The results also show that the overall water quality of the Yellow River Basin is good and coincident with the actual situation of the Yellow River basin. Overall, gray clustering method for water quality evaluation is reasonable and feasible and it is also convenient to calculate.

Applications of Cluster Analysis to the Creation of Perfectionism Profiles: A Comparison of two Clustering Approaches

Directory of Open Access Journals (Sweden)

Jocelyn H Bolin

2014-04-01

Full Text Available Although traditional clustering methods (e.g., K-means have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Applications of cluster analysis to the creation of perfectionism profiles: a comparison of two clustering approaches.

Science.gov (United States)

Bolin, Jocelyn H; Edwards, Julianne M; Finch, W Holmes; Cassady, Jerrell C

2014-01-01

Although traditional clustering methods (e.g., K-means) have been shown to be useful in the social sciences it is often difficult for such methods to handle situations where clusters in the population overlap or are ambiguous. Fuzzy clustering, a method already recognized in many disciplines, provides a more flexible alternative to these traditional clustering methods. Fuzzy clustering differs from other traditional clustering methods in that it allows for a case to belong to multiple clusters simultaneously. Unfortunately, fuzzy clustering techniques remain relatively unused in the social and behavioral sciences. The purpose of this paper is to introduce fuzzy clustering to these audiences who are currently relatively unfamiliar with the technique. In order to demonstrate the advantages associated with this method, cluster solutions of a common perfectionism measure were created using both fuzzy clustering and K-means clustering, and the results compared. Results of these analyses reveal that different cluster solutions are found by the two methods, and the similarity between the different clustering solutions depends on the amount of cluster overlap allowed for in fuzzy clustering.

Clustering self-organizing maps (SOM) method for human papillomavirus (HPV) DNA as the main cause of cervical cancer disease

Science.gov (United States)

Bustamam, A.; Aldila, D.; Fatimah, Arimbi, M. D.

2017-07-01

One of the most widely used clustering method, since it has advantage on its robustness, is Self-Organizing Maps (SOM) method. This paper discusses the application of SOM method on Human Papillomavirus (HPV) DNA which is the main cause of cervical cancer disease, the most dangerous cancer in developing countries. We use 18 types of HPV DNA-based on the newest complete genome. By using open-source-based program R, clustering process can separate 18 types of HPV into two different clusters. There are two types of HPV in the first cluster while 16 others in the second cluster. The analyzing result of 18 types HPV based on the malignancy of the virus (the difficultness to cure). Two of HPV types the first cluster can be classified as tame HPV, while 16 others in the second cluster are classified as vicious HPV.

A New Waveform Signal Processing Method Based on Adaptive Clustering-Genetic Algorithms

International Nuclear Information System (INIS)

Noha Shaaban; Fukuzo Masuda; Hidetsugu Morota

2006-01-01

We present a fast digital signal processing method for numerical analysis of individual pulses from CdZnTe compound semiconductor detectors. Using Maxi-Mini Distance Algorithm and Genetic Algorithms based discrimination technique. A parametric approach has been used for classifying the discriminated waveforms into a set of clusters each has a similar signal shape with a corresponding pulse height spectrum. A corrected total pulse height spectrum was obtained by applying a normalization factor for the full energy peak for each cluster with a highly improvements in the energy spectrum characteristics. This method applied successfully for both simulated and real measured data, it can be applied to any detector suffers from signal shape variation. (authors)

Clustering method for counting passengers getting in a bus with single camera

Science.gov (United States)

Yang, Tao; Zhang, Yanning; Shao, Dapei; Li, Ying

2010-03-01

Automatic counting of passengers is very important for both business and security applications. We present a single-camera-based vision system that is able to count passengers in a highly crowded situation at the entrance of a traffic bus. The unique characteristics of the proposed system include, First, a novel feature-point-tracking- and online clustering-based passenger counting framework, which performs much better than those of background-modeling-and foreground-blob-tracking-based methods. Second, a simple and highly accurate clustering algorithm is developed that projects the high-dimensional feature point trajectories into a 2-D feature space by their appearance and disappearance times and counts the number of people through online clustering. Finally, all test video sequences in the experiment are captured from a real traffic bus in Shanghai, China. The results show that the system can process two 320×240 video sequences at a frame rate of 25 fps simultaneously, and can count passengers reliably in various difficult scenarios with complex interaction and occlusion among people. The method achieves high accuracy rates up to 96.5%.

DLTAP: A Network-efficient Scheduling Method for Distributed Deep Learning Workload in Containerized Cluster Environment

Directory of Open Access Journals (Sweden)

Qiao Wei

2017-01-01

Full Text Available Deep neural networks (DNNs have recently yielded strong results on a range of applications. Training these DNNs using a cluster of commodity machines is a promising approach since training is time consuming and compute-intensive. Furthermore, putting DNN tasks into containers of clusters would enable broader and easier deployment of DNN-based algorithms. Toward this end, this paper addresses the problem of scheduling DNN tasks in the containerized cluster environment. Efficiently scheduling data-parallel computation jobs like DNN over containerized clusters is critical for job performance, system throughput, and resource utilization. It becomes even more challenging with the complex workloads. We propose a scheduling method called Deep Learning Task Allocation Priority (DLTAP which performs scheduling decisions in a distributed manner, and each of scheduling decisions takes aggregation degree of parameter sever task and worker task into account, in particularly, to reduce cross-node network transmission traffic and, correspondingly, decrease the DNN training time. We evaluate the DLTAP scheduling method using a state-of-the-art distributed DNN training framework on 3 benchmarks. The results show that the proposed method can averagely reduce 12% cross-node network traffic, and decrease the DNN training time even with the cluster of low-end servers.

Clustering analysis

International Nuclear Information System (INIS)

Romli

1997-01-01

Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K -mean method ' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

Clustering Methods; Part IV of Scientific Report No. ISR-18, Information Storage and Retrieval...

Science.gov (United States)

Cornell Univ., Ithaca, NY. Dept. of Computer Science.

Two papers are included as Part Four of this report on Salton's Magical Automatic Retriever of Texts (SMART) project report. The first paper: "A Controlled Single Pass Classification Algorithm with Application to Multilevel Clustering" by D. B. Johnson and J. M. Laferente presents a single pass clustering method which compares favorably…

A multi-attribute Systemic Risk Index for comparing and prioritizing chemical industrial areas

International Nuclear Information System (INIS)

Reniers, G.L.L.; Sörensen, K.; Dullaert, W.

2012-01-01

Measures taken to decrease interdependent risks within chemical industrial areas should be based on quantitative data from a holistic (cluster-based) point of view. Therefore, this paper examines the typology of networks representing industrial areas to formulate recommendations to more effectively protect a chemical cluster against existing systemic risks. Chemical industrial areas are modeled as two distinct complex networks and are prioritized by computing two sub-indices with respect to existing systemic safety and security risks (using Domino Danger Units) and supply chain risks (using units from an ordinal expert scale). Subsequently, a Systemic Risk Index for the industrial area is determined employing the Borda algorithm, whereby the systemic risk index considers both a safety and security network risk index and a supply chain network risk index. The developed method allows decreasing systemic risks within chemical industrial areas from a holistic (inter-organizational and/or inter-cluster) perspective. An illustrative example is given.

Analysis of ligand-protein exchange by Clustering of Ligand Diffusion Coefficient Pairs (CoLD-CoP)

Science.gov (United States)

Snyder, David A.; Chantova, Mihaela; Chaudhry, Saadia

2015-06-01

NMR spectroscopy is a powerful tool in describing protein structures and protein activity for pharmaceutical and biochemical development. This study describes a method to determine weak binding ligands in biological systems by using hierarchic diffusion coefficient clustering of multidimensional data obtained with a 400 MHz Bruker NMR. Comparison of DOSY spectrums of ligands of the chemical library in the presence and absence of target proteins show translational diffusion rates for small molecules upon interaction with macromolecules. For weak binders such as compounds found in fragment libraries, changes in diffusion rates upon macromolecular binding are on the order of the precision of DOSY diffusion measurements, and identifying such subtle shifts in diffusion requires careful statistical analysis. The "CoLD-CoP" (Clustering of Ligand Diffusion Coefficient Pairs) method presented here uses SAHN clustering to identify protein-binders in a chemical library or even a not fully characterized metabolite mixture. We will show how DOSY NMR and the "CoLD-CoP" method complement each other in identifying the most suitable candidates for lysozyme and wheat germ acid phosphatase.

Designing continuous safety improvement within chemical industrial areas

NARCIS (Netherlands)

Reniers, G.L.L.; Ale, B. J.M.; Dullaert, W.; Soudan, K.

This article provides support in organizing and implementing novel concepts for enhancing safety on a cluster level of chemical plants. The paper elaborates the requirements for integrating Safety Management Systems of chemical plants situated within a so-called chemical cluster. Recommendations of

Brightest Cluster Galaxies in REXCESS Clusters

Science.gov (United States)

Haarsma, Deborah B.; Leisman, L.; Bruch, S.; Donahue, M.

2009-01-01

Most galaxy clusters contain a Brightest Cluster Galaxy (BCG) which is larger than the other cluster ellipticals and has a more extended profile. In the hierarchical model, the BCG forms through many galaxy mergers in the crowded center of the cluster, and thus its properties give insight into the assembly of the cluster as a whole. In this project, we are working with the Representative XMM-Newton Cluster Structure Survey (REXCESS) team (Boehringer et al 2007) to study BCGs in 33 X-ray luminous galaxy clusters, 0.055 < z < 0.183. We are imaging the BCGs in R band at the Southern Observatory for Astrophysical Research (SOAR) in Chile. In this poster, we discuss our methods and give preliminary measurements of the BCG magnitudes, morphology, and stellar mass. We compare these BCG properties with the properties of their host clusters, particularly of the X-ray emitting gas.

Anionic water pentamer and hexamer clusters: An extensive study of structures and energetics

Science.gov (United States)

Ünal, Aslı; Bozkaya, Uǧur

2018-03-01

An extensive study of structures and energetics for anionic pentamer and hexamer clusters is performed employing high level ab initio quantum chemical methods, such as the density-fitted orbital-optimized linearized coupled-cluster doubles (DF-OLCCD), coupled-cluster singles and doubles (CCSD), and coupled-cluster singles and doubles with perturbative triples [CCSD(T)] methods. In this study, sixteen anionic pentamer clusters and eighteen anionic hexamer clusters are reported. Relative, binding, and vertical detachment energies (VDE) are presented at the complete basis set limit (CBS), extrapolating energies of aug4-cc-pVTZ and aug4-cc-pVQZ custom basis sets. The largest VDE values obtained at the CCSD(T)/CBS level are 9.9 and 11.2 kcal mol-1 for pentamers and hexamers, respectively, which are in very good agreement with the experimental values of 9.5 and 11.1 kcal mol-1. Our binding energy results, at the CCSD(T)/CBS level, indicate strong bindings in anionic clusters due to hydrogen bond interactions. The average binding energy per water molecules is -5.0 and -5.3 kcal mol-1 for pentamers and hexamers, respectively. Furthermore, our results demonstrate that the DF-OLCCD method approaches to the CCSD(T) quality for anionic clusters. The inexpensive analytic gradients of DF-OLCCD compared to CCSD or CCSD(T) make it very attractive for high-accuracy studies.

Combining microscopy with spectroscopic and chemical methods for tracing the origin of atmospheric fallouts from mining sites

Energy Technology Data Exchange (ETDEWEB)

Navel, Aline; Uzu, Gaëlle; Spadini, Lorenzo [University Grenoble Alpes — LTHE UMR 5564–CNRS-INSU/UGA/INPG/IRD, 1025 rue de la Piscine, DU BP53 - 38041 Grenoble CEDEX 9 (France); Sobanska, Sophie [LASIR, (UMR CNRS 8516), Université de Lille 1, Bât. C5, 59655 Villeneuve d' Ascq CEDEX (France); Martins, Jean M.F., E-mail: jean.martins@yujf-grenoble.fr [University Grenoble Alpes — LTHE UMR 5564–CNRS-INSU/UGA/INPG/IRD, 1025 rue de la Piscine, DU BP53 - 38041 Grenoble CEDEX 9 (France)

2015-12-30

Highlights: • Numerous ancient mines are left over without specific care for contaminated wastes. • Sources similarity makes the tracing of the origin of metallic fallouts challenging. • Physico-chemical fingerprints of all metal-source sites and fallouts were established. • Combining physical/chemical methods allowed discriminating polluted fallouts origin. • A Hierarchical cluster analysis permitted to identify the dominant particles source. - Abstract: Populations living close to mining sites are often exposed to important heavy metal concentrations, especially through atmospheric fallouts. Identifying the main sources of metal-rich particles remains a challenge because of the similarity of the particle signatures from the polluted sites. This work provides an original combination of physical and chemical methods to determine the main sources of airborne particles impacting inhabited zones. Raman microspectrometry (RMS), X-ray diffraction (DRX), morphology analyses by microscopy and chemical composition were assessed. Geochemical analysis allowed the identification of target and source areas; XRD and RMS analysis identified the main mineral phases in association with their metal content and speciation. The characterization of the dominant minerals was combined with particle morphology analysis to identify fallout sources. The complete description of dust morphologies permitted the successful determination of a fingerprint of each source site. The analysis of these chemical and morphological fingerprints allowed identification of the mine area as the main contributor of metal-rich particles impacting the inhabited zone. In addition to the identification of the main sources of airborne particles, this study will also permit to better define the extent of polluted zones requiring remediation or protection from eolian erosion inducing metal-rich atmospheric fallouts.

Combining microscopy with spectroscopic and chemical methods for tracing the origin of atmospheric fallouts from mining sites

International Nuclear Information System (INIS)

Navel, Aline; Uzu, Gaëlle; Spadini, Lorenzo; Sobanska, Sophie; Martins, Jean M.F.

2015-01-01

Highlights: • Numerous ancient mines are left over without specific care for contaminated wastes. • Sources similarity makes the tracing of the origin of metallic fallouts challenging. • Physico-chemical fingerprints of all metal-source sites and fallouts were established. • Combining physical/chemical methods allowed discriminating polluted fallouts origin. • A Hierarchical cluster analysis permitted to identify the dominant particles source. - Abstract: Populations living close to mining sites are often exposed to important heavy metal concentrations, especially through atmospheric fallouts. Identifying the main sources of metal-rich particles remains a challenge because of the similarity of the particle signatures from the polluted sites. This work provides an original combination of physical and chemical methods to determine the main sources of airborne particles impacting inhabited zones. Raman microspectrometry (RMS), X-ray diffraction (DRX), morphology analyses by microscopy and chemical composition were assessed. Geochemical analysis allowed the identification of target and source areas; XRD and RMS analysis identified the main mineral phases in association with their metal content and speciation. The characterization of the dominant minerals was combined with particle morphology analysis to identify fallout sources. The complete description of dust morphologies permitted the successful determination of a fingerprint of each source site. The analysis of these chemical and morphological fingerprints allowed identification of the mine area as the main contributor of metal-rich particles impacting the inhabited zone. In addition to the identification of the main sources of airborne particles, this study will also permit to better define the extent of polluted zones requiring remediation or protection from eolian erosion inducing metal-rich atmospheric fallouts.

Modeling and Analysis of a Spectrum of the Globular Cluster NGC 2419

OpenAIRE

Sharina, M. E.; Shimansky, V. V.; Davoust, E.

2012-01-01

NGC 2419 is the most distant massive globular cluster in the outer Galactic halo. It is unusual also due to the chemical peculiarities found in its red giant stars in recent years. We study the stellar population of this unusual object using spectra obtained at the 1.93-m telescope of the Haute-Provence Observatory. At variance with commonly used methods of high-resolution spectroscopy applicable only to bright stars, we employ spectroscopic information on the integrated light of the cluster....

A robust automatic leukocyte recognition method based on island-clustering texture

Directory of Open Access Journals (Sweden)

Xiaoshun Li

2016-01-01

Full Text Available A leukocyte recognition method for human peripheral blood smear based on island-clustering texture (ICT is proposed. By analyzing the features of the five typical classes of leukocyte images, a new ICT model is established. Firstly, some feature points are extracted in a gray leukocyte image by mean-shift clustering to be the centers of islands. Secondly, the growing region is employed to create regions of the islands in which the seeds are just these feature points. These islands distribution can describe a new texture. Finally, a distinguished parameter vector of these islands is created as the ICT features by combining the ICT features with the geometric features of the leukocyte. Then the five typical classes of leukocytes can be recognized successfully at the correct recognition rate of more than 92.3% with a total sample of 1310 leukocytes. Experimental results show the feasibility of the proposed method. Further analysis reveals that the method is robust and results can provide important information for disease diagnosis.

Atypical Mg-poor Milky Way Field Stars with Globular Cluster Second-generation-like Chemical Patterns

Energy Technology Data Exchange (ETDEWEB)

Fernández-Trincado, J. G.; Geisler, D.; Tang, B.; Villanova, S.; Mennickent, R. E. [Departamento de Astronomía, Universidad de Concepción, Casilla 160-C, Concepción (Chile); Zamora, O.; García-Hernández, D. A.; Dell’Agli, F.; Prieto, Carlos Allende [Instituto de Astrofísica de Canarias, E-38205 La Laguna, Tenerife (Spain); Souto, Diogo; Cunha, Katia [Observatório Nacional, Rua Gal. José Cristino 77, Rio de Janeiro, RJ—20921-400 (Brazil); Schiavon, R. P. [Astrophysics Research Institute, Liverpool John Moores University, 146 Brownlow Hill, Liverpool L3 5RF (United Kingdom); Hasselquist, Sten [New Mexico State University, Las Cruces, NM 88003 (United States); Shetrone, M. [University of Texas at Austin, McDonald Observatory, Fort Davis, TX 79734 (United States); Vieira, K. [Centro de Investigaciones de Astronomía, AP 264, Mérida 5101-A (Venezuela, Bolivarian Republic of); Zasowski, G. [Space Telescope Science Institute, 3700 San Martin Drive, Baltimore, MD 21218 (United States); Sobeck, J.; Hayes, C. R.; Majewski, S. R. [Department of Astronomy, University of Virginia, Charlottesville, VA 22903 (United States); Placco, V. M., E-mail: jfernandezt@astro-udec.cl, E-mail: jfernandezt87@gmail.com [Department of Physics and JINA Center for the Evolution of the Elements, University of Notre Dame, Notre Dame, IN 46556 (United States); and others

2017-09-01

We report the peculiar chemical abundance patterns of 11 atypical Milky Way (MW) field red giant stars observed by the Apache Point Observatory Galactic Evolution Experiment (APOGEE). These atypical giants exhibit strong Al and N enhancements accompanied by C and Mg depletions, strikingly similar to those observed in the so-called second-generation (SG) stars of globular clusters (GCs). Remarkably, we find low Mg abundances ([Mg/Fe] < 0.0) together with strong Al and N overabundances in the majority (5/7) of the metal-rich ([Fe/H] ≳ −1.0) sample stars, which is at odds with actual observations of SG stars in Galactic GCs of similar metallicities. This chemical pattern is unique and unprecedented among MW stars, posing urgent questions about its origin. These atypical stars could be former SG stars of dissolved GCs formed with intrinsically lower abundances of Mg and enriched Al (subsequently self-polluted by massive AGB stars) or the result of exotic binary systems. We speculate that the stars Mg-deficiency as well as the orbital properties suggest that they could have an extragalactic origin. This discovery should guide future dedicated spectroscopic searches of atypical stellar chemical patterns in our Galaxy, a fundamental step forward to understanding the Galactic formation and evolution.

X-ray spectroscopy of clusters of galaxies and of the cosmic web

NARCIS (Netherlands)

Werner, N.

2008-01-01

I present the results on the study of the chemical evolution of the intra-cluster medium (ICM) and on the evolution of clusters of galaxies in the context of the cosmic web. Clusters of galaxies are excellent laboratories to study the chemical enrichment history of the Universe. This thesis presents

Relativistic rise measurement by cluster counting method in time expansion chamber

International Nuclear Information System (INIS)

Rehak, P.; Walenta, A.H.

1979-10-01

A new approach to the measurement of the ionization energy loss for the charged particle identification in the region of the relativistic rise was tested experimentally. The method consists of determining in a special drift chamber (TEC) the number of clusters of the primary ionization. The method gives almost the full relativistic rise and narrower landau distribution. The consequences for a practical detector are discussed

Spatial cluster modelling

CERN Document Server

Lawson, Andrew B

2002-01-01

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

Control of neuronal network organization by chemical surface functionalization of multi-walled carbon nanotube arrays

International Nuclear Information System (INIS)

Liu Jie; Bibari, Olivier; Marchand, Gilles; Benabid, Alim-Louis; Sauter-Starace, Fabien; Appaix, Florence; De Waard, Michel

2011-01-01

Carbon nanotube substrates are promising candidates for biological applications and devices. Interfacing of these carbon nanotubes with neurons can be controlled by chemical modifications. In this study, we investigated how chemical surface functionalization of multi-walled carbon nanotube arrays (MWNT-A) influences neuronal adhesion and network organization. Functionalization of MWNT-A dramatically modifies the length of neurite fascicles, cluster inter-connection success rate, and the percentage of neurites that escape from the clusters. We propose that chemical functionalization represents a method of choice for developing applications in which neuronal patterning on MWNT-A substrates is required.

Control of neuronal network organization by chemical surface functionalization of multi-walled carbon nanotube arrays

Energy Technology Data Exchange (ETDEWEB)

Liu Jie; Bibari, Olivier; Marchand, Gilles; Benabid, Alim-Louis; Sauter-Starace, Fabien [CEA, LETI-Minatec, 17 Rue des Martyrs, 38054 Grenoble Cedex 9 (France); Appaix, Florence; De Waard, Michel, E-mail: fabien.sauter@cea.fr, E-mail: michel.dewaard@ujf-grenoble.fr [Inserm U836, Grenoble Institute of Neuroscience, Site Sante la Tronche, Batiment Edmond J Safra, Chemin Fortune Ferrini, BP170, 38042 Grenoble Cedex 09 (France)

2011-05-13

Carbon nanotube substrates are promising candidates for biological applications and devices. Interfacing of these carbon nanotubes with neurons can be controlled by chemical modifications. In this study, we investigated how chemical surface functionalization of multi-walled carbon nanotube arrays (MWNT-A) influences neuronal adhesion and network organization. Functionalization of MWNT-A dramatically modifies the length of neurite fascicles, cluster inter-connection success rate, and the percentage of neurites that escape from the clusters. We propose that chemical functionalization represents a method of choice for developing applications in which neuronal patterning on MWNT-A substrates is required.

Pre-crash scenarios at road junctions: A clustering method for car crash data.

Science.gov (United States)

Nitsche, Philippe; Thomas, Pete; Stuetz, Rainer; Welsh, Ruth

2017-10-01

Given the recent advancements in autonomous driving functions, one of the main challenges is safe and efficient operation in complex traffic situations such as road junctions. There is a need for comprehensive testing, either in virtual simulation environments or on real-world test tracks. This paper presents a novel data analysis method including the preparation, analysis and visualization of car crash data, to identify the critical pre-crash scenarios at T- and four-legged junctions as a basis for testing the safety of automated driving systems. The presented method employs k-medoids to cluster historical junction crash data into distinct partitions and then applies the association rules algorithm to each cluster to specify the driving scenarios in more detail. The dataset used consists of 1056 junction crashes in the UK, which were exported from the in-depth "On-the-Spot" database. The study resulted in thirteen crash clusters for T-junctions, and six crash clusters for crossroads. Association rules revealed common crash characteristics, which were the basis for the scenario descriptions. The results support existing findings on road junction accidents and provide benchmark situations for safety performance tests in order to reduce the possible number parameter combinations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cluster Physics with Merging Galaxy Clusters

Directory of Open Access Journals (Sweden)

Sandor M. Molnar

2016-02-01

Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Plasmon resonances in large noble-metal clusters

International Nuclear Information System (INIS)

Soennichsen, C; Franzl, T; Wilk, T; Plessen, G von; Feldmann, J

2002-01-01

We investigate the optical properties of spherical gold and silver clusters with diameters of 20 nm and larger. The light scattering spectra of individual clusters are measured using dark-field microscopy, thus avoiding inhomogeneous broadening effects. The dipolar plasmon resonances of the clusters are found to have nearly Lorentzian line shapes. With increasing size we observe polaritonic red-shifts of the plasmon line and increased radiation damping for both gold and silver clusters. Apart from some cluster-to-cluster variations of the plasmon lines, agreement with Mie theory is reasonably good for the gold clusters. However, it is less satisfactory for the silver clusters, possibly due to cluster faceting or chemical effects

GLOBULAR CLUSTER ABUNDANCES FROM HIGH-RESOLUTION, INTEGRATED-LIGHT SPECTROSCOPY. IV. THE LARGE MAGELLANIC CLOUD: α, Fe-PEAK, LIGHT, AND HEAVY ELEMENTS

International Nuclear Information System (INIS)

Colucci, Janet E.; Bernstein, Rebecca A.; Cameron, Scott A.; McWilliam, Andrew

2012-01-01

We present detailed chemical abundances in eight clusters in the Large Magellanic Cloud (LMC). We measure abundances of 22 elements for clusters spanning a range in age of 0.05-12 Gyr, providing a comprehensive picture of the chemical enrichment and star formation history of the LMC. The abundances were obtained from individual absorption lines using a new method for analysis of high-resolution (R ∼ 25,000), integrated-light (IL) spectra of star clusters. This method was developed and presented in Papers I, II, and III of this series. In this paper, we develop an additional IL χ 2 -minimization spectral synthesis technique to facilitate measurement of weak (∼15 mÅ) spectral lines and abundances in low signal-to-noise ratio data (S/N ∼ 30). Additionally, we supplement the IL abundance measurements with detailed abundances that we measure for individual stars in the youngest clusters (age +0.5) and increases with decreasing age, indicating a strong contribution of low-metallicity asymptotic giant branch star ejecta to the interstellar medium throughout the later history of the LMC. We also find a correlation of IL Na and Al abundances with cluster mass in the sense that more massive, older clusters are enriched in the light elements Na and Al with respect to Fe, which implies that these clusters harbor star-to-star abundance variations as is common in the MW. Lower mass, intermediate-age, and young clusters have Na and Al abundances that are lower and more consistent with LMC field stars. Our results can be used to constrain both future chemical evolution models for the LMC and theories of globular cluster formation.

IP2P K-means: an efficient method for data clustering on sensor networks

Directory of Open Access Journals (Sweden)

Peyman Mirhadi

2013-03-01

Full Text Available Many wireless sensor network applications require data gathering as the most important parts of their operations. There are increasing demands for innovative methods to improve energy efficiency and to prolong the network lifetime. Clustering is considered as an efficient topology control methods in wireless sensor networks, which can increase network scalability and lifetime. This paper presents a method, IP2P K-means – Improved P2P K-means, which uses efficient leveling in clustering approach, reduces false labeling and restricts the necessary communication among various sensors, which obviously saves more energy. The proposed method is examined in Network Simulator Ver.2 (NS2 and the preliminary results show that the algorithm works effectively and relatively more precisely.

Clustering the Orion B giant molecular cloud based on its molecular emission.

Science.gov (United States)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also

A Comparison of Methods for Player Clustering via Behavioral Telemetry

DEFF Research Database (Denmark)

Drachen, Anders; Thurau, C.; Sifa, R.

2013-01-01

patterns in the behavioral data, and developing profiles that are actionable to game developers. There are numerous methods for unsupervised clustering of user behavior, e.g. k-means/c-means, Nonnegative Matrix Factorization, or Principal Component Analysis. Although all yield behavior categorizations......, interpretation of the resulting categories in terms of actual play behavior can be difficult if not impossible. In this paper, a range of unsupervised techniques are applied together with Archetypal Analysis to develop behavioral clusters from playtime data of 70,014 World of Warcraft players, covering a five......The analysis of user behavior in digital games has been aided by the introduction of user telemetry in game development, which provides unprecedented access to quantitative data on user behavior from the installed game clients of the entire population of players. Player behavior telemetry datasets...

Comprehensive cluster analysis with Transitivity Clustering.

Science.gov (United States)

Wittkop, Tobias; Emig, Dorothea; Truss, Anke; Albrecht, Mario; Böcker, Sebastian; Baumbach, Jan

2011-03-01

Transitivity Clustering is a method for the partitioning of biological data into groups of similar objects, such as genes, for instance. It provides integrated access to various functions addressing each step of a typical cluster analysis. To facilitate this, Transitivity Clustering is accessible online and offers three user-friendly interfaces: a powerful stand-alone version, a web interface, and a collection of Cytoscape plug-ins. In this paper, we describe three major workflows: (i) protein (super)family detection with Cytoscape, (ii) protein homology detection with incomplete gold standards and (iii) clustering of gene expression data. This protocol guides the user through the most important features of Transitivity Clustering and takes ∼1 h to complete.

Fourth-order perturbative extension of the single-double excitation coupled-cluster method

International Nuclear Information System (INIS)

Derevianko, Andrei; Emmons, Erik D.

2002-01-01

Fourth-order many-body corrections to matrix elements for atoms with one valence electron are derived. The obtained diagrams are classified using coupled-cluster-inspired separation into contributions from n-particle excitations from the lowest-order wave function. The complete set of fourth-order diagrams involves only connected single, double, and triple excitations and disconnected quadruple excitations. Approximately half of the fourth-order diagrams are not accounted for by the popular coupled-cluster method truncated at single and double excitations (CCSD). Explicit formulas are tabulated for the entire set of fourth-order diagrams missed by the CCSD method and its linearized version, i.e., contributions from connected triple and disconnected quadruple excitations. A partial summation scheme of the derived fourth-order contributions to all orders of perturbation theory is proposed

Functional connectivity analysis of the neural bases of emotion regulation: A comparison of independent component method with density-based k-means clustering method.

Science.gov (United States)

Zou, Ling; Guo, Qian; Xu, Yi; Yang, Biao; Jiao, Zhuqing; Xiang, Jianbo

2016-04-29

Functional magnetic resonance imaging (fMRI) is an important tool in neuroscience for assessing connectivity and interactions between distant areas of the brain. To find and characterize the coherent patterns of brain activity as a means of identifying brain systems for the cognitive reappraisal of the emotion task, both density-based k-means clustering and independent component analysis (ICA) methods can be applied to characterize the interactions between brain regions involved in cognitive reappraisal of emotion. Our results reveal that compared with the ICA method, the density-based k-means clustering method provides a higher sensitivity of polymerization. In addition, it is more sensitive to those relatively weak functional connection regions. Thus, the study concludes that in the process of receiving emotional stimuli, the relatively obvious activation areas are mainly distributed in the frontal lobe, cingulum and near the hypothalamus. Furthermore, density-based k-means clustering method creates a more reliable method for follow-up studies of brain functional connectivity.

Recycling of poly(ethylene terephthalate – A review focusing on chemical methods

Directory of Open Access Journals (Sweden)

B. Geyer

2016-07-01

Full Text Available Recycling of poly(ethylene terephthalate (PET is of crucial importance, since worldwide amounts of PETwaste increase rapidly due to its widespread applications. Hence, several methods have been developed, like energetic, material, thermo-mechanical and chemical recycling of PET. Most frequently, PET-waste is incinerated for energy recovery, used as additive in concrete composites or glycolysed to yield mixtures of monomers and undefined oligomers. While energetic and thermo-mechanical recycling entail downcycling of the material, chemical recycling requires considerable amounts of chemicals and demanding processing steps entailing toxic and ecological issues. This review provides a thorough survey of PET-recycling including energetic, material, thermo-mechanical and chemical methods. It focuses on chemical methods describing important reaction parameters and yields of obtained reaction products. While most methods yield monomers, only a few yield undefined low molecular weight oligomers for impaired applications (dispersants or plasticizers. Further, the present work presents an alternative chemical recycling method of PET in comparison to existing chemical methods.

Cluster monte carlo method for nuclear criticality safety calculation

International Nuclear Information System (INIS)

Pei Lucheng

1984-01-01

One of the most important applications of the Monte Carlo method is the calculation of the nuclear criticality safety. The fair source game problem was presented at almost the same time as the Monte Carlo method was applied to calculating the nuclear criticality safety. The source iteration cost may be reduced as much as possible or no need for any source iteration. This kind of problems all belongs to the fair source game prolems, among which, the optimal source game is without any source iteration. Although the single neutron Monte Carlo method solved the problem without the source iteration, there is still quite an apparent shortcoming in it, that is, it solves the problem without the source iteration only in the asymptotic sense. In this work, a new Monte Carlo method called the cluster Monte Carlo method is given to solve the problem further

Globular clusters - Fads and fallacies

International Nuclear Information System (INIS)

White, R.E.

1991-01-01

The types of globular clusters observed in the Milky Way Galaxy are described together with their known characteristics, with special attention given to correcting the erroneous statements made earlier about globular clusters. Among these are the following statements: the Galaxy is surrounded by many hundreds of globular clusters; all globular clusters are located toward the Galactic center, all globular clusters are metal poor and move about the Galaxy in highly elliptical paths; all globular clusters contain RR Lyrae-type variable stars, and the RR Lyrae stars found outside of globulars have come from cluster dissolution or ejection; all of the stars in a given cluster were born at the same time and have the same chemical composition; X-ray globulars are powered by central black holes; and the luminosity functions for globular clusters are well defined and well determined. Consideration is given to the fact that globular clusters in the Magellanic Clouds differ from those in the Milky Way by their age distribution and that the globulars of the SMC differ from those of the LMC

Chemical characterization by INAA of Brazilian ceramics and cultural implications

International Nuclear Information System (INIS)

Munita, C.S.; Paiva, R.P.; Momose, E.F.; Saiki, M.; Alves, M.A.

2000-01-01

Archaeological ceramic fragments from Agua Limpa site, in Sao Paulo, Brazil, were analyzed using instrumental neutron activation analysis. Multivariate statistical methods including Pearson correlation coefficient, cluster and principal components analysis were used to interpret the concentration data. Rare earth and alkaline elements were highly correlated. Six principal components explained 74.9% of the total variance and five clusters were found. The sample chemical composition showed that all samples have the same provenance. (author)

Single-cluster dynamics for the random-cluster model

NARCIS (Netherlands)

Deng, Y.; Qian, X.; Blöte, H.W.J.

2009-01-01

We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

Data Clustering

Science.gov (United States)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

Consumers' Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms.

Science.gov (United States)

Yang, Yan-Pu; Chen, Deng-Kai; Gu, Rong; Gu, Yu-Feng; Yu, Sui-Huai

2016-01-01

Consumers' Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design.

A Spectrum Sensing Method Based on Signal Feature and Clustering Algorithm in Cognitive Wireless Multimedia Sensor Networks

Directory of Open Access Journals (Sweden)

Yongwei Zhang

2017-01-01

Full Text Available In order to solve the problem of difficulty in determining the threshold in spectrum sensing technologies based on the random matrix theory, a spectrum sensing method based on clustering algorithm and signal feature is proposed for Cognitive Wireless Multimedia Sensor Networks. Firstly, the wireless communication signal features are obtained according to the sampling signal covariance matrix. Then, the clustering algorithm is used to classify and test the signal features. Different signal features and clustering algorithms are compared in this paper. The experimental results show that the proposed method has better sensing performance.

Multi-Optimisation Consensus Clustering

Science.gov (United States)

Li, Jian; Swift, Stephen; Liu, Xiaohui

Ensemble Clustering has been developed to provide an alternative way of obtaining more stable and accurate clustering results. It aims to avoid the biases of individual clustering algorithms. However, it is still a challenge to develop an efficient and robust method for Ensemble Clustering. Based on an existing ensemble clustering method, Consensus Clustering (CC), this paper introduces an advanced Consensus Clustering algorithm called Multi-Optimisation Consensus Clustering (MOCC), which utilises an optimised Agreement Separation criterion and a Multi-Optimisation framework to improve the performance of CC. Fifteen different data sets are used for evaluating the performance of MOCC. The results reveal that MOCC can generate more accurate clustering results than the original CC algorithm.

A Novel Double Cluster and Principal Component Analysis-Based Optimization Method for the Orbit Design of Earth Observation Satellites

Directory of Open Access Journals (Sweden)

Yunfeng Dong

2017-01-01

Full Text Available The weighted sum and genetic algorithm-based hybrid method (WSGA-based HM, which has been applied to multiobjective orbit optimizations, is negatively influenced by human factors through the artificial choice of the weight coefficients in weighted sum method and the slow convergence of GA. To address these two problems, a cluster and principal component analysis-based optimization method (CPC-based OM is proposed, in which many candidate orbits are gradually randomly generated until the optimal orbit is obtained using a data mining method, that is, cluster analysis based on principal components. Then, the second cluster analysis of the orbital elements is introduced into CPC-based OM to improve the convergence, developing a novel double cluster and principal component analysis-based optimization method (DCPC-based OM. In DCPC-based OM, the cluster analysis based on principal components has the advantage of reducing the human influences, and the cluster analysis based on six orbital elements can reduce the search space to effectively accelerate convergence. The test results from a multiobjective numerical benchmark function and the orbit design results of an Earth observation satellite show that DCPC-based OM converges more efficiently than WSGA-based HM. And DCPC-based OM, to some degree, reduces the influence of human factors presented in WSGA-based HM.

A SOM clustering pattern sequence-based next symbol prediction method for day-ahead direct electricity load and price forecasting

International Nuclear Information System (INIS)

Jin, Cheng Hao; Pok, Gouchol; Lee, Yongmi; Park, Hyun-Woo; Kim, Kwang Deuk; Yun, Unil; Ryu, Keun Ho

2015-01-01

Highlights: • A novel pattern sequence-based direct time series forecasting method was proposed. • Due to the use of SOM’s topology preserving property, only SOM can be applied. • SCPSNSP only deals with the cluster patterns not each specific time series value. • SCPSNSP performs better than recently developed forecasting algorithms. - Abstract: In this paper, we propose a new day-ahead direct time series forecasting method for competitive electricity markets based on clustering and next symbol prediction. In the clustering step, pattern sequence and their topology relations are obtained from self organizing map time series clustering. In the next symbol prediction step, with each cluster label in the pattern sequence represented as a pair of its topologically identical coordinates, artificial neural network is used to predict the topological coordinates of next day by training the relationship between previous daily pattern sequence and its next day pattern. According to the obtained topology relations, the nearest nonzero hits pattern is assigned to next day so that the whole time series values can be directly forecasted from the assigned cluster pattern. The proposed method was evaluated on Spanish, Australian and New York electricity markets and compared with PSF and some of the most recently published forecasting methods. Experimental results show that the proposed method outperforms the best forecasting methods at least 3.64%

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2006-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

Cluster fusion algorithm: application to Lennard-Jones clusters

DEFF Research Database (Denmark)

Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

2008-01-01

paths up to the cluster size of 150 atoms. We demonstrate that in this way all known global minima structures of the Lennard-Jones clusters can be found. Our method provides an efficient tool for the calculation and analysis of atomic cluster structure. With its use we justify the magic number sequence......We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing...... for the clusters of noble gas atoms and compare it with experimental observations. We report the striking correspondence of the peaks in the dependence of the second derivative of the binding energy per atom on cluster size calculated for the chain of the Lennard-Jones clusters based on the icosahedral symmetry...

The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods

Science.gov (United States)

Oemler Jr., Augustus; Dressler, Alan; Gladders, Michael G.; Rigby, Jane R.; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M.;

THE IMACS CLUSTER BUILDING SURVEY. I. DESCRIPTION OF THE SURVEY AND ANALYSIS METHODS

Energy Technology Data Exchange (ETDEWEB)

Oemler, Augustus Jr.; Dressler, Alan; Kelson, Daniel; Villanueva, Edward [Observatories of the Carnegie Institution for Science, 813 Santa Barbara St., Pasadena, CA 91101-1292 (United States); Gladders, Michael G. [Department of Astronomy and Astrophysics, University of Chicago, Chicago, IL 60637 (United States); Rigby, Jane R. [Observational Cosmology Lab, NASA Goddard Space Flight Center, Greenbelt, MD 20771 (United States); Bai Lei [Department of Astronomy and Astrophysics, University of Toronto, 50 St. George Street, Toronto, ON M5S 3H4 (Canada); Fritz, Jacopo [Sterrenkundig Observatorium, Universiteit Gent, Krijgslaan 281 S9, B-9000 Gent (Belgium); Rieke, George [Steward Observatory, University of Arizona, Tucson, AZ 8572 (United States); Poggianti, Bianca M.; Vulcani, Benedetta, E-mail: oemler@obs.carnegiescience.edu [INAF-Osservatorio Astronomico di Padova, Vicolo dell' Osservatorio 5, I-35122 Padova (Italy)

2013-06-10

The IMACS Cluster Building Survey uses the wide field spectroscopic capabilities of the IMACS spectrograph on the 6.5 m Baade Telescope to survey the large-scale environment surrounding rich intermediate-redshift clusters of galaxies. The goal is to understand the processes which may be transforming star-forming field galaxies into quiescent cluster members as groups and individual galaxies fall into the cluster from the surrounding supercluster. This first paper describes the survey: the data taking and reduction methods. We provide new calibrations of star formation rates (SFRs) derived from optical and infrared spectroscopy and photometry. We demonstrate that there is a tight relation between the observed SFR per unit B luminosity, and the ratio of the extinctions of the stellar continuum and the optical emission lines. With this, we can obtain accurate extinction-corrected colors of galaxies. Using these colors as well as other spectral measures, we determine new criteria for the existence of ongoing and recent starbursts in galaxies.

Clustering of carboxylated magnetite nanoparticles through polyethylenimine: Covalent versus electrostatic approach

Energy Technology Data Exchange (ETDEWEB)

Tóth, Ildikó Y., E-mail: Ildiko.Toth@chem.u-szeged.hu [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Nesztor, Dániel [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Novák, Levente [Department of Colloid and Environmental Chemistry, University of Debrecen, Egyetem square 1, Debrecen (Hungary); Illés, Erzsébet; Szekeres, Márta; Szabó, Tamás [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary); Tombácz, Etelka, E-mail: tombacz@chem.u-szeged.hu [Department of Physical Chemistry and Materials Science, University of Szeged, Aradi vt. square 1, Szeged (Hungary)

2017-04-01

Carboxylated magnetite nanoparticles (MNPs) are frequently used to develop materials with enhanced properties for MRI and hyperthermia. The controlled clustering of MNPs via covalent or electrostatic approaches provides opportunity to prepare high quality materials. MNPs were prepared by co-precipitation and coated by poly(acrylic acid-co-maleic acid) (PAM@MNP). The clusters were synthesized from purified PAM@MNPs and polyethylenimine (PEI) solution via electrostatic interaction and covalent bond formation (ES-cluster and CB-cluster, respectively). The electrostatic adhesion (–NH{sub 3}{sup +} and –COO{sup –}) and the formed amide bond were confirmed by ATR-FTIR. The averaged area of CB-clusters was about twice as large as that of ES-cluster, based on TEM. The SAXS results showed that the surface of MNPs was smooth and the nanoparticles were close packed in both clusters. The pH-dependent aggregation state and zeta potential of clusters were characterized by DLS and electrophoresis measurements, the clusters were colloidally stable at pH>5. In hyperthermia experiments, the values of SAR were about two times larger for the chemically bonded cluster. The MRI studies showed exceptionally high transversion relaxivities, the r{sub 2} values are 457 mM{sup −1} s{sup −1} and 691 mM{sup −1} s{sup −1} for ES-cluster and CB-cluster, respectively. Based on these results, the chemically clustered product shows greater potential for feasible biomedical applications. - Highlights: • Chemically bonded clusters (CB-cluster) were prepared from PEI and PAM-coated MNPs. • The electrostatically clustered units (ES-cluster) are smaller and more compact. • The electrostatic adhesion and the amide bond formation were confirmed by ATR-FTIR. • CB-cluster dispersions are colloidally stable under physiological conditions. • CB-cluster shows great potential for application in MRI and hyperthermia.

VBSCF Methods: Classical Chemical Concepts and Beyond

NARCIS (Netherlands)

Rashid, Z.

2013-01-01

The aim of this research has been to extend the ab initio Valence Bond Self-Consistent Field (VBSCF) methodology and to apply this method to the electronic structure of molecules. The valence bond method directly deals with the chemical structure of molecules in a pictorial language, which chemists

A comparison of confidence interval methods for the intraclass correlation coefficient in community-based cluster randomization trials with a binary outcome.

Science.gov (United States)

Braschel, Melissa C; Svec, Ivana; Darlington, Gerarda A; Donner, Allan

2016-04-01

Many investigators rely on previously published point estimates of the intraclass correlation coefficient rather than on their associated confidence intervals to determine the required size of a newly planned cluster randomized trial. Although confidence interval methods for the intraclass correlation coefficient that can be applied to community-based trials have been developed for a continuous outcome variable, fewer methods exist for a binary outcome variable. The aim of this study is to evaluate confidence interval methods for the intraclass correlation coefficient applied to binary outcomes in community intervention trials enrolling a small number of large clusters. Existing methods for confidence interval construction are examined and compared to a new ad hoc approach based on dividing clusters into a large number of smaller sub-clusters and subsequently applying existing methods to the resulting data. Monte Carlo simulation is used to assess the width and coverage of confidence intervals for the intraclass correlation coefficient based on Smith's large sample approximation of the standard error of the one-way analysis of variance estimator, an inverted modified Wald test for the Fleiss-Cuzick estimator, and intervals constructed using a bootstrap-t applied to a variance-stabilizing transformation of the intraclass correlation coefficient estimate. In addition, a new approach is applied in which clusters are randomly divided into a large number of smaller sub-clusters with the same methods applied to these data (with the exception of the bootstrap-t interval, which assumes large cluster sizes). These methods are also applied to a cluster randomized trial on adolescent tobacco use for illustration. When applied to a binary outcome variable in a small number of large clusters, existing confidence interval methods for the intraclass correlation coefficient provide poor coverage. However, confidence intervals constructed using the new approach combined with Smith

Globular Cluster Abundances from High-resolution, Integrated-light Spectroscopy. IV. The Large Magellanic Cloud: α, Fe-peak, Light, and Heavy Elements

Science.gov (United States)

Colucci, Janet E.; Bernstein, Rebecca A.; Cameron, Scott A.; McWilliam, Andrew

2012-02-01

We present detailed chemical abundances in eight clusters in the Large Magellanic Cloud (LMC). We measure abundances of 22 elements for clusters spanning a range in age of 0.05-12 Gyr, providing a comprehensive picture of the chemical enrichment and star formation history of the LMC. The abundances were obtained from individual absorption lines using a new method for analysis of high-resolution (R ~ 25,000), integrated-light (IL) spectra of star clusters. This method was developed and presented in Papers I, II, and III of this series. In this paper, we develop an additional IL χ2-minimization spectral synthesis technique to facilitate measurement of weak (~15 mÅ) spectral lines and abundances in low signal-to-noise ratio data (S/N ~ 30). Additionally, we supplement the IL abundance measurements with detailed abundances that we measure for individual stars in the youngest clusters (age +0.5) and increases with decreasing age, indicating a strong contribution of low-metallicity asymptotic giant branch star ejecta to the interstellar medium throughout the later history of the LMC. We also find a correlation of IL Na and Al abundances with cluster mass in the sense that more massive, older clusters are enriched in the light elements Na and Al with respect to Fe, which implies that these clusters harbor star-to-star abundance variations as is common in the MW. Lower mass, intermediate-age, and young clusters have Na and Al abundances that are lower and more consistent with LMC field stars. Our results can be used to constrain both future chemical evolution models for the LMC and theories of globular cluster formation. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

Unequal cluster sizes in stepped-wedge cluster randomised trials: a systematic review.

Science.gov (United States)

Kristunas, Caroline; Morris, Tom; Gray, Laura

2017-11-15

To investigate the extent to which cluster sizes vary in stepped-wedge cluster randomised trials (SW-CRT) and whether any variability is accounted for during the sample size calculation and analysis of these trials. Any, not limited to healthcare settings. Any taking part in an SW-CRT published up to March 2016. The primary outcome is the variability in cluster sizes, measured by the coefficient of variation (CV) in cluster size. Secondary outcomes include the difference between the cluster sizes assumed during the sample size calculation and those observed during the trial, any reported variability in cluster sizes and whether the methods of sample size calculation and methods of analysis accounted for any variability in cluster sizes. Of the 101 included SW-CRTs, 48% mentioned that the included clusters were known to vary in size, yet only 13% of these accounted for this during the calculation of the sample size. However, 69% of the trials did use a method of analysis appropriate for when clusters vary in size. Full trial reports were available for 53 trials. The CV was calculated for 23 of these: the median CV was 0.41 (IQR: 0.22-0.52). Actual cluster sizes could be compared with those assumed during the sample size calculation for 14 (26%) of the trial reports; the cluster sizes were between 29% and 480% of that which had been assumed. Cluster sizes often vary in SW-CRTs. Reporting of SW-CRTs also remains suboptimal. The effect of unequal cluster sizes on the statistical power of SW-CRTs needs further exploration and methods appropriate to studies with unequal cluster sizes need to be employed. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Thermodynamics of non-ideal QGP using Mayers cluster expansion method

International Nuclear Information System (INIS)

Prasanth, J.P; Simji, P.; Bannur, Vishnu M.

2013-01-01

The Quark gluon plasma (QGP) is the state in which the individual hadrons dissolve into a system of free (or almost free) quarks and gluons in strongly compressed system at high temperature. The present paper aims to calculate the critical temperature at which a non-ideal three quark plasma condenses into droplet of three quarks (i.e., into a liquid of baryons) using Mayers cluster expansion method

Research of the Space Clustering Method for the Airport Noise Data Minings

Directory of Open Access Journals (Sweden)

Jiwen Xie

2014-03-01

Full Text Available Mining the distribution pattern and evolution of the airport noise from the airport noise data and the geographic information of the monitoring points is of great significance for the scientific and rational governance of airport noise pollution problem. However, most of the traditional clustering methods are based on the closeness of space location or the similarity of non-spatial features, which split the duality of space elements, resulting in that the clustering result has difficult in satisfying both the closeness of space location and the similarity of non-spatial features. This paper, therefore, proposes a spatial clustering algorithm based on dual-distance. This algorithm uses a distance function as the similarity measure function in which spatial features and non-spatial features are combined. The experimental results show that the proposed algorithm can discover the noise distribution pattern around the airport effectively.

[Bioinorganic chemical composition of the lens and methods of its investigation].

Science.gov (United States)

Avetisov, S E; Novikov, I A; Pakhomova, N A; Motalov, V G

2018-01-01

Bioinorganic chemical composition of the lens of human and experimental animals (cows, dogs, rats, rabbits) have been analyzed in various studies. In most cases, the studies employed different methods to determine the gross (total) composition of chemical elements and their concentrations in the examined samples. Less frequently, they included an assessment of the distribution of chemical elements in the lens and correlation of their concentration with its morphological changes. Chemical elements from all groups (series) of the periodic classification system were discovered in the lens substance. Despite similar investigation methods, different authors obtained contradicting results on the chemical composition of the lens. This article presents data suggesting possible correlation between inorganic chemical elements in the lens substance with the development and formation of lenticular opacities. All currently employed methods are known to only analyze limited number of select chemical elements in the tissues and do not consider the whole range of elements that can be analyzed with existing technology; furthermore, the majority of studies are conducted on the animal model lens. Therefore, it is feasible to continue the development of the chemical microanalysis method by increasing the sensitivity of Scanning Electron Microscopy with Energy Dispersive Spectroscopy (SEM/EDS) with the purpose of assessing the gross chemical composition and distribution of the elements in the lens substance, as well as revealing possible correlation between element concentration and morphological changes in the lens.

New Target for an Old Method: Hubble Measures Globular Cluster Parallax

Science.gov (United States)

Hensley, Kerry

2018-05-01

Measuring precise distances to faraway objects has long been a challenge in astrophysics. Now, one of the earliest techniques used to measure the distance to astrophysical objects has been applied to a metal-poor globular cluster for the first time.A Classic TechniqueAn artists impression of the European Space Agencys Gaia spacecraft. Gaia is on track to map the positions and motions of a billion stars. [ESA]Distances to nearby stars are often measured using the parallax technique tracing the tiny apparent motion of a target star against the background of more distant stars as Earth orbits the Sun. This technique has come a long way since it was first used in the 1800s to measure the distance to stars a few tens of light-years away; with the advent of space observatories like Hipparcos and Gaia, parallax can now be used to map the positions of stars out to thousands of light-years.Precise distance measurements arent only important for setting the scale of the universe, however; they can also help us better understand stellar evolution over the course of cosmic history. Stellar evolution models are often anchored to a reference star cluster, the properties of which must be known precisely. These precise properties can be readily determined for young, nearby open clusters using parallax measurements. But stellar evolution models that anchor on themore-distant, ancient, metal-poor globular clusters have been hampered by theless-precise indirect methods used tomeasure distance to these faraway clusters until now.Top: An image of NGC 6397 overlaid with the area scanned by Hubble (dashed green) and the footprint of the camera (solid green). The blue ellipse represents the parallax motion of a star in the cluster, exaggerated by a factor of ten thousand. Bottom: An example scan from this field. [Adapted from Brown et al. 2018]New Measurement to an Old ClusterThomas Brown (Space Telescope Science Institute) and collaborators used the Hubble Space Telescope todetermine the

A simple and fast method to determine the parameters for fuzzy c-means cluster analysis

DEFF Research Database (Denmark)

Schwämmle, Veit; Jensen, Ole Nørregaard

2010-01-01

MOTIVATION: Fuzzy c-means clustering is widely used to identify cluster structures in high-dimensional datasets, such as those obtained in DNA microarray and quantitative proteomics experiments. One of its main limitations is the lack of a computationally fast method to set optimal values...... of algorithm parameters. Wrong parameter values may either lead to the inclusion of purely random fluctuations in the results or ignore potentially important data. The optimal solution has parameter values for which the clustering does not yield any results for a purely random dataset but which detects cluster...... formation with maximum resolution on the edge of randomness. RESULTS: Estimation of the optimal parameter values is achieved by evaluation of the results of the clustering procedure applied to randomized datasets. In this case, the optimal value of the fuzzifier follows common rules that depend only...

Simple method to calculate percolation, Ising and Potts clusters

International Nuclear Information System (INIS)

Tsallis, C.

1981-01-01

A procedure ('break-collapse method') is introduced which considerably simplifies the calculation of two - or multirooted clusters like those commonly appearing in real space renormalization group (RG) treatments of bond-percolation, and pure and random Ising and Potts problems. The method is illustrated through two applications for the q-state Potts ferromagnet. The first of them concerns a RG calculation of the critical exponent ν for the isotropic square lattice: numerical consistence is obtained (particularly for q→0) with den Nijs conjecture. The second application is a compact reformulation of the standard star-triangle and duality transformations which provide the exact critical temperature for the anisotropic triangular and honeycomb lattices. (Author) [pt

Method of cleaning oil slicks and chemical spills

International Nuclear Information System (INIS)

Billings, L.

1992-01-01

This patent describes a method of cleaning a floating chemical spill on a body of water. It comprises: providing a quantity of popular bark-based pelleted or granular product, flotation means and a flexible net having openings generally smaller than the smallest whole pellet dimension of the pelleted product, spreading the net over a chemical spill on the body of water, connecting the floatation means to the net thereby supporting the net adjacent the surface of the body of water, placing the poplar bark-based product on the net, absorbing the floating chemical spill into the product, and removing the chemical soaked product from the body of water

Calculation of Multiphase Chemical Equilibrium by the Modified RAND Method

DEFF Research Database (Denmark)

Tsanas, Christos; Stenby, Erling Halfdan; Yan, Wei

2017-01-01

method. The modified RAND extends the classical RAND method from single-phase chemical reaction equilibrium of ideal systems to multiphase chemical equilibrium of nonideal systems. All components in all phases are treated in the same manner and the system Gibbs energy can be used to monitor convergence....... This is the first time that modified RAND was applied to multiphase chemical equilibrium systems. The combined algorithm was tested using nine examples covering vapor–liquid (VLE) and vapor–liquid–liquid equilibria (VLLE) of ideal and nonideal reaction systems. Successive substitution provided good initial......A robust and efficient algorithm for simultaneous chemical and phase equilibrium calculations is proposed. It combines two individual nonstoichiometric solving procedures: a nested-loop method with successive substitution for the first steps and final convergence with the second-order modified RAND...

Clustering in the stellar abundance space

Science.gov (United States)

Boesso, R.; Rocha-Pinto, H. J.

2018-03-01

We have studied the chemical enrichment history of the interstellar medium through an analysis of the n-dimensional stellar abundance space. This work is a non-parametric analysis of the stellar chemical abundance space. The main goal is to study the stars from their organization within this abundance space. Within this space, we seek to find clusters (in a statistical sense), that is, stars likely to share similar chemo-evolutionary history, using two methods: the hierarchical clustering and the principal component analysis. We analysed some selected abundance surveys available in the literature. For each sample, we labelled the group of stars according to its average abundance curve. In all samples, we identify the existence of a main enrichment pattern of the stars, which we call chemical enrichment flow. This flow is set by the structured and well-defined mean rate at which the abundances of the interstellar medium increase, resulting from the mixture of the material ejected from the stars and stellar mass-loss and interstellar medium gas. One of the main results of our analysis is the identification of subgroups of stars with peculiar chemistry. These stars are situated in regions outside of the enrichment flow in the abundance space. These peculiar stars show a mismatch in the enrichment rate of a few elements, such as Mg, Si, Sc and V, when compared to the mean enrichment rate of the other elements of the same stars. We believe that the existence of these groups of stars with peculiar chemistry may be related to the accretion of planetary material on to stellar surfaces or may be due to production of the same chemical element by different nucleosynthetic sites.

Chemical Diversity in Basil (Ocimum sp.) Germplasm

Science.gov (United States)

da Costa, Andréa Santos; Arrigoni-Blank, Maria de Fátima; de Carvalho Filho, José Luiz Sandes; de Santana, Aléa Dayane Dantas; Santos, Darlisson de Alexandria; Alves, Péricles Barreto; Blank, Arie Fitzgerald

2015-01-01

The present study aimed to chemically characterize 31 accessions and seven cultivars of basil. The percentage composition of the essential oils of the accessions and cultivars was based on the 14 most abundant constituents: 1,8-cineole, linalool, methyl chavicol, neral, nerol, geraniol, geranial, methyl cinnamate, β-bourbonene, methyl eugenol, α-trans-bergamotene, germacrene-D, epi-α-cadinol, and δ-cadinene. The genetic materials were classified into eight clusters according to the chemical composition of the essential oils: Cluster 1—mostly linalool and 1,8-cineole; Cluster 2—mostly linalool, geraniol, and α-trans-bergamotene; Cluster 3—mostly linalool, methyl chavicol, methyl cinnamate, and β-bourbonene; Cluster 4—mostly linalool, methyl chavicol, epi-α-cadinol, and α-trans-bergamotene; Cluster 5—mainly linalool, methyl eugenol, α-trans-bergamotene, and epi-α-cadinol; Cluster 6—mainly linalool, geraniol, and epi-α-cadinol; Cluster 7—mostly linalool and methyl chavicol; Cluster 8—mainly geranial and neral. PMID:25629084

Chemical Diversity in Basil (Ocimum sp. Germplasm

Directory of Open Access Journals (Sweden)

Andréa Santos da Costa

2015-01-01

Full Text Available The present study aimed to chemically characterize 31 accessions and seven cultivars of basil. The percentage composition of the essential oils of the accessions and cultivars was based on the 14 most abundant constituents: 1,8-cineole, linalool, methyl chavicol, neral, nerol, geraniol, geranial, methyl cinnamate, β-bourbonene, methyl eugenol, α-trans-bergamotene, germacrene-D, epi-α-cadinol, and δ-cadinene. The genetic materials were classified into eight clusters according to the chemical composition of the essential oils: Cluster 1—mostly linalool and 1,8-cineole; Cluster 2—mostly linalool, geraniol, and α-trans-bergamotene; Cluster 3—mostly linalool, methyl chavicol, methyl cinnamate, and β-bourbonene; Cluster 4—mostly linalool, methyl chavicol, epi-α-cadinol, and α-trans-bergamotene; Cluster 5—mainly linalool, methyl eugenol, α-trans-bergamotene, and epi-α-cadinol; Cluster 6—mainly linalool, geraniol, and epi-α-cadinol; Cluster 7—mostly linalool and methyl chavicol; Cluster 8—mainly geranial and neral.

Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

Directory of Open Access Journals (Sweden)

Lee Yun-Shien

2008-03-01

Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

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